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• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

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Researcher, Academic Writer, Web developer

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• Roberta Heale 1 ,
• Alison Twycross 2
• 1 School of Nursing , Laurentian University , Sudbury , Ontario , Canada
• 2 School of Health and Social Care , London South Bank University , London , UK
• Correspondence to Dr Roberta Heale, School of Nursing, Laurentian University, Sudbury, ON P3E2C6, Canada; rheale{at}laurentian.ca

https://doi.org/10.1136/eb-2017-102845

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What is it?

Case study is a research methodology, typically seen in social and life sciences. There is no one definition of case study research. 1 However, very simply… ‘a case study can be defined as an intensive study about a person, a group of people or a unit, which is aimed to generalize over several units’. 1 A case study has also been described as an intensive, systematic investigation of a single individual, group, community or some other unit in which the researcher examines in-depth data relating to several variables. 2

Often there are several similar cases to consider such as educational or social service programmes that are delivered from a number of locations. Although similar, they are complex and have unique features. In these circumstances, the evaluation of several, similar cases will provide a better answer to a research question than if only one case is examined, hence the multiple-case study. Stake asserts that the cases are grouped and viewed as one entity, called the quintain . 6  ‘We study what is similar and different about the cases to understand the quintain better’. 6

The steps when using case study methodology are the same as for other types of research. 6 The first step is defining the single case or identifying a group of similar cases that can then be incorporated into a multiple-case study. A search to determine what is known about the case(s) is typically conducted. This may include a review of the literature, grey literature, media, reports and more, which serves to establish a basic understanding of the cases and informs the development of research questions. Data in case studies are often, but not exclusively, qualitative in nature. In multiple-case studies, analysis within cases and across cases is conducted. Themes arise from the analyses and assertions about the cases as a whole, or the quintain, emerge. 6

Benefits and limitations of case studies

If a researcher wants to study a specific phenomenon arising from a particular entity, then a single-case study is warranted and will allow for a in-depth understanding of the single phenomenon and, as discussed above, would involve collecting several different types of data. This is illustrated in example 1 below.

Using a multiple-case research study allows for a more in-depth understanding of the cases as a unit, through comparison of similarities and differences of the individual cases embedded within the quintain. Evidence arising from multiple-case studies is often stronger and more reliable than from single-case research. Multiple-case studies allow for more comprehensive exploration of research questions and theory development. 6

Despite the advantages of case studies, there are limitations. The sheer volume of data is difficult to organise and data analysis and integration strategies need to be carefully thought through. There is also sometimes a temptation to veer away from the research focus. 2 Reporting of findings from multiple-case research studies is also challenging at times, 1 particularly in relation to the word limits for some journal papers.

Examples of case studies

Example 1: nurses’ paediatric pain management practices.

One of the authors of this paper (AT) has used a case study approach to explore nurses’ paediatric pain management practices. This involved collecting several datasets:

Observational data to gain a picture about actual pain management practices.

Questionnaire data about nurses’ knowledge about paediatric pain management practices and how well they felt they managed pain in children.

Questionnaire data about how critical nurses perceived pain management tasks to be.

These datasets were analysed separately and then compared 7–9 and demonstrated that nurses’ level of theoretical did not impact on the quality of their pain management practices. 7 Nor did individual nurse’s perceptions of how critical a task was effect the likelihood of them carrying out this task in practice. 8 There was also a difference in self-reported and observed practices 9 ; actual (observed) practices did not confirm to best practice guidelines, whereas self-reported practices tended to.

Example 2: quality of care for complex patients at Nurse Practitioner-Led Clinics (NPLCs)

The other author of this paper (RH) has conducted a multiple-case study to determine the quality of care for patients with complex clinical presentations in NPLCs in Ontario, Canada. 10 Five NPLCs served as individual cases that, together, represented the quatrain. Three types of data were collected including:

Review of documentation related to the NPLC model (media, annual reports, research articles, grey literature and regulatory legislation).

Interviews with nurse practitioners (NPs) practising at the five NPLCs to determine their perceptions of the impact of the NPLC model on the quality of care provided to patients with multimorbidity.

Chart audits conducted at the five NPLCs to determine the extent to which evidence-based guidelines were followed for patients with diabetes and at least one other chronic condition.

The three sources of data collected from the five NPLCs were analysed and themes arose related to the quality of care for complex patients at NPLCs. The multiple-case study confirmed that nurse practitioners are the primary care providers at the NPLCs, and this positively impacts the quality of care for patients with multimorbidity. Healthcare policy, such as lack of an increase in salary for NPs for 10 years, has resulted in issues in recruitment and retention of NPs at NPLCs. This, along with insufficient resources in the communities where NPLCs are located and high patient vulnerability at NPLCs, have a negative impact on the quality of care. 10

These examples illustrate how collecting data about a single case or multiple cases helps us to better understand the phenomenon in question. Case study methodology serves to provide a framework for evaluation and analysis of complex issues. It shines a light on the holistic nature of nursing practice and offers a perspective that informs improved patient care.

• Gustafsson J
• Calanzaro M
• Sandelowski M

Competing interests None declared.

Provenance and peer review Commissioned; internally peer reviewed.

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Case Study Methods and Examples

By Janet Salmons, PhD Manager, Sage Research Methods Community

What is Case Study Methodology ?

Case studies in research are both unique and uniquely confusing. The term case study is confusing because the same term is used multiple ways. The term can refer to the methodology, that is, a system of frameworks used to design a study, or the methods used to conduct it. Or, case study can refer to a type of academic writing that typically delves into a problem, process, or situation.

Case study methodology can entail the study of one or more "cases," that could be described as instances, examples, or settings where the problem or phenomenon can be examined. The researcher is tasked with defining the parameters of the case, that is, what is included and excluded. This process is called bounding the case , or setting boundaries.

Case study can be combined with other methodologies, such as ethnography, grounded theory, or phenomenology. In such studies the research on the case uses another framework to further define the study and refine the approach.

Case study is also described as a method, given particular approaches used to collect and analyze data. Case study research is conducted by almost every social science discipline: business, education, sociology, psychology. Case study research, with its reliance on multiple sources, is also a natural choice for researchers interested in trans-, inter-, or cross-disciplinary studies.

The Encyclopedia of case study research provides an overview:

The purpose of case study research is twofold: (1) to provide descriptive information and (2) to suggest theoretical relevance. Rich description enables an in-depth or sharpened understanding of the case.

It is unique given one characteristic: case studies draw from more than one data source. Case studies are inherently multimodal or mixed methods because this they use either more than one form of data within a research paradigm, or more than one form of data from different paradigms.

A case study inquiry could include multiple types of data:

multiple forms of quantitative data sources, such as Big Data + a survey

multiple forms of qualitative data sources, such as interviews + observations + documents

multiple forms of quantitative and qualitative data sources, such as surveys + interviews

Case study methodology can be used to achieve different research purposes.

Robert Yin , methodologist most associated with case study research, differentiates between descriptive , exploratory and explanatory case studies:

Descriptive : A case study whose purpose is to describe a phenomenon. Explanatory : A case study whose purpose is to explain how or why some condition came to be, or why some sequence of events occurred or did not occur. Exploratory: A case study whose purpose is to identify the research questions or procedures to be used in a subsequent study.

Robert Yin’s book is a comprehensive guide for case study researchers!

You can read the preface and Chapter 1 of Yin's book here . See the open-access articles below for some published examples of qualitative, quantitative, and mixed methods case study research.

Mills, A. J., Durepos, G., & Wiebe, E. (2010).  Encyclopedia of case study research (Vols. 1-0). Thousand Oaks, CA: SAGE Publications, Inc. doi: 10.4135/9781412957397

Yin, R. K. (2018). Case study research and applications (6th ed.). Thousand Oaks: SAGE Publications.

Open-Access Articles Using Case Study Methodology

As you can see from this collection, case study methods are used in qualitative, quantitative and mixed methods research.

Ang, C.-S., Lee, K.-F., & Dipolog-Ubanan, G. F. (2019). Determinants of First-Year Student Identity and Satisfaction in Higher Education: A Quantitative Case Study. SAGE Open. https://doi.org/10.1177/2158244019846689

Abstract. First-year undergraduates’ expectations and experience of university and student engagement variables were investigated to determine how these perceptions influence their student identity and overall course satisfaction. Data collected from 554 first-year undergraduates at a large private university were analyzed. Participants were given the adapted version of the Melbourne Centre for the Study of Higher Education Survey to self-report their learning experience and engagement in the university community. The results showed that, in general, the students’ reasons of pursuing tertiary education were to open the door to career opportunities and skill development. Moreover, students’ views on their learning and university engagement were at the moderate level. In relation to student identity and overall student satisfaction, it is encouraging to state that their perceptions of studentship and course satisfaction were rather positive. After controlling for demographics, student engagement appeared to explain more variance in student identity, whereas students’ expectations and experience explained greater variance in students’ overall course satisfaction. Implications for practice, limitations, and recommendation of this study are addressed.

Baker, A. J. (2017). Algorithms to Assess Music Cities: Case Study—Melbourne as a Music Capital. SAGE Open. https://doi.org/10.1177/2158244017691801

Abstract. The global  Mastering of a Music City  report in 2015 notes that the concept of music cities has penetrated the global political vernacular because it delivers “significant economic, employment, cultural and social benefits.” This article highlights that no empirical study has combined all these values and offers a relevant and comprehensive definition of a music city. Drawing on industry research,1 the article assesses how mathematical flowcharts, such as Algorithm A (Economics), Algorithm B (Four T’s creative index), and Algorithm C (Heritage), have contributed to the definition of a music city. Taking Melbourne as a case study, it illustrates how Algorithms A and B are used as disputed evidence about whether the city is touted as Australia’s music capital. The article connects the three algorithms to an academic framework from musicology, urban studies, cultural economics, and sociology, and proposes a benchmark Algorithm D (Music Cities definition), which offers a more holistic assessment of music activity in any urban context. The article concludes by arguing that Algorithm D offers a much-needed definition of what comprises a music city because it builds on the popular political economy focus and includes the social importance of space and cultural practices.

Brown, K., & Mondon, A. (2020). Populism, the media, and the mainstreaming of the far right: The Guardian’s coverage of populism as a case study. Politics. https://doi.org/10.1177/0263395720955036

Abstract. Populism seems to define our current political age. The term is splashed across the headlines, brandished in political speeches and commentaries, and applied extensively in numerous academic publications and conferences. This pervasive usage, or populist hype, has serious implications for our understanding of the meaning of populism itself and for our interpretation of the phenomena to which it is applied. In particular, we argue that its common conflation with far-right politics, as well as its breadth of application to other phenomena, has contributed to the mainstreaming of the far right in three main ways: (1) agenda-setting power and deflection, (2) euphemisation and trivialisation, and (3) amplification. Through a mixed-methods approach to discourse analysis, this article uses  The Guardian  newspaper as a case study to explore the development of the populist hype and the detrimental effects of the logics that it has pushed in public discourse.

Droy, L. T., Goodwin, J., & O’Connor, H. (2020). Methodological Uncertainty and Multi-Strategy Analysis: Case Study of the Long-Term Effects of Government Sponsored Youth Training on Occupational Mobility. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 147–148(1–2), 200–230. https://doi.org/10.1177/0759106320939893

Abstract. Sociological practitioners often face considerable methodological uncertainty when undertaking a quantitative analysis. This methodological uncertainty encompasses both data construction (e.g. defining variables) and analysis (e.g. selecting and specifying a modelling procedure). Methodological uncertainty can lead to results that are fragile and arbitrary. Yet, many practitioners may be unaware of the potential scale of methodological uncertainty in quantitative analysis, and the recent emergence of techniques for addressing it. Recent proposals for ‘multi-strategy’ approaches seek to identify and manage methodological uncertainty in quantitative analysis. We present a case-study of a multi-strategy analysis, applied to the problem of estimating the long-term impact of 1980s UK government-sponsored youth training. We use this case study to further highlight the problem of cumulative methodological fragilities in applied quantitative sociology and to discuss and help develop multi-strategy analysis as a tool to address them.

Ebneyamini, S., & Sadeghi Moghadam, M. R. (2018). Toward Developing a Framework for Conducting Case Study Research .  International Journal of Qualitative Methods .  https://doi.org/10.1177/1609406918817954

Abstract. This article reviews the use of case study research for both practical and theoretical issues especially in management field with the emphasis on management of technology and innovation. Many researchers commented on the methodological issues of the case study research from their point of view thus, presenting a comprehensive framework was missing. We try representing a general framework with methodological and analytical perspective to design, develop, and conduct case study research. To test the coverage of our framework, we have analyzed articles in three major journals related to the management of technology and innovation to approve our framework. This study represents a general structure to guide, design, and fulfill a case study research with levels and steps necessary for researchers to use in their research.

Lai, D., & Roccu, R. (2019). Case study research and critical IR: the case for the extended case methodology. International Relations , 33 (1), 67-87. https://doi.org/10.1177/0047117818818243

Abstract. Discussions on case study methodology in International Relations (IR) have historically been dominated by positivist and neopositivist approaches. However, these are problematic for critical IR research, pointing to the need for a non-positivist case study methodology. To address this issue, this article introduces and adapts the extended case methodology as a critical, reflexivist approach to case study research, whereby the case is constructed through a dynamic interaction with theory, rather than selected, and knowledge is produced through extensions rather than generalisation. Insofar as it seeks to study the world in complex and non-linear terms, take context and positionality seriously, and generate explicitly political and emancipatory knowledge, the extended case methodology is consistent with the ontological and epistemological commitments of several critical IR approaches. Its potential is illustrated in the final part of the article with reference to researching the socioeconomic dimension of transitional justice in Bosnia and Herzegovina.

Lynch, R., Young, J. C., Boakye-Achampong, S., Jowaisas, C., Sam, J., & Norlander, B. (2020). Benefits of crowdsourcing for libraries: A case study from Africa . IFLA Journal. https://doi.org/10.1177/0340035220944940

Abstract. Many libraries in the Global South do not collect comprehensive data about themselves, which creates challenges in terms of local and international visibility. Crowdsourcing is an effective tool that engages the public to collect missing data, and it has proven to be particularly valuable in countries where governments collect little public data. Whereas crowdsourcing is often used within fields that have high levels of development funding, such as health, the authors believe that this approach would have many benefits for the library field as well. They present qualitative and quantitative evidence from 23 African countries involved in a crowdsourcing project to map libraries. The authors find benefits in terms of increased connections between stakeholders, capacity-building, and increased local visibility. These findings demonstrate the potential of crowdsourced approaches for tasks such as mapping to benefit libraries and similarly positioned institutions in the Global South in multifaceted ways.

Mason, W., Morris, K., Webb, C., Daniels, B., Featherstone, B., Bywaters, P., Mirza, N., Hooper, J., Brady, G., Bunting, L., & Scourfield, J. (2020). Toward Full Integration of Quantitative and Qualitative Methods in Case Study Research: Insights From Investigating Child Welfare Inequalities. Journal of Mixed Methods Research, 14 (2), 164-183. https://doi.org/10.1177/1558689819857972

Abstract. Delineation of the full integration of quantitative and qualitative methods throughout all stages of multisite mixed methods case study projects remains a gap in the methodological literature. This article offers advances to the field of mixed methods by detailing the application and integration of mixed methods throughout all stages of one such project; a study of child welfare inequalities. By offering a critical discussion of site selection and the management of confirmatory, expansionary and discordant data, this article contributes to the limited body of mixed methods exemplars specific to this field. We propose that our mixed methods approach provided distinctive insights into a complex social problem, offering expanded understandings of the relationship between poverty, child abuse, and neglect.

Rashid, Y., Rashid, A., Warraich, M. A., Sabir, S. S., & Waseem, A. (2019). Case Study Method: A Step-by-Step Guide for Business Researchers .  International Journal of Qualitative Methods .  https://doi.org/10.1177/1609406919862424

Abstract. Qualitative case study methodology enables researchers to conduct an in-depth exploration of intricate phenomena within some specific context. By keeping in mind research students, this article presents a systematic step-by-step guide to conduct a case study in the business discipline. Research students belonging to said discipline face issues in terms of clarity, selection, and operationalization of qualitative case study while doing their final dissertation. These issues often lead to confusion, wastage of valuable time, and wrong decisions that affect the overall outcome of the research. This article presents a checklist comprised of four phases, that is, foundation phase, prefield phase, field phase, and reporting phase. The objective of this article is to provide novice researchers with practical application of this checklist by linking all its four phases with the authors’ experiences and learning from recently conducted in-depth multiple case studies in the organizations of New Zealand. Rather than discussing case study in general, a targeted step-by-step plan with real-time research examples to conduct a case study is given.

VanWynsberghe, R., & Khan, S. (2007). Redefining Case Study. International Journal of Qualitative Methods, 80–94. https://doi.org/10.1177/160940690700600208

Abstract. In this paper the authors propose a more precise and encompassing definition of case study than is usually found. They support their definition by clarifying that case study is neither a method nor a methodology nor a research design as suggested by others. They use a case study prototype of their own design to propose common properties of case study and demonstrate how these properties support their definition. Next, they present several living myths about case study and refute them in relation to their definition. Finally, they discuss the interplay between the terms case study and unit of analysis to further delineate their definition of case study. The target audiences for this paper include case study researchers, research design and methods instructors, and graduate students interested in case study research.

More Sage Research Methods Community Posts about Case Study Research

Use research cases as the basis for individual or team activities that build skills.

Find an 10-step process for using research cases to teach methods with learning activities for individual students, teams, or small groups. (Or use the approach yourself!)

How do you decide which methodology fits your study? In this dialogue Linda Bloomberg and Janet Boberg explain the importance of a strategic approach to qualitative research design that stresses alignment with the purpose of the study.

Case study methods are used by researchers in many disciplines. Here are some open-access articles about multimodal qualitative or mixed methods designs that include both qualitative and quantitative elements.

Case study methodology is both unique, and uniquely confusing. It is unique given one characteristic: case studies draw from more than one data source.

What is case study methodology? It is unique given one characteristic: case studies draw from more than one data source. In this post find definitions and a collection of multidisciplinary examples.

Find discussion of case studies and published examples.

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Experiments and quantitative research.

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Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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What is a case study?

A case study is a type of research method. In case studies, the unit of analysis is a case . The case typically provides a detailed account of a situation that usually focuses on a conflict or complexity that one might encounter in the workplace.

• Case studies help explain the process by which a unit (a person, department, business, organization, industry, country, etc.) deals with the issue or problem confronting it, and offers possible solutions that can be applied to other units facing similar situations.
• The information presented in case studies is usually qualitative in nature - gathered through methods such as interview, observation, and document collection.
• There are different types of case study, including  intrinsic, instrumental, naturalistic,  and  pragmatic.

This research guide will assist you in finding individual case studies, as well as providing information on designing case studies. If you need assistance locating information, please Ask a Librarian .

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A case study research paper examines a person, place, event, condition, phenomenon, or other type of subject of analysis in order to extrapolate  key themes and results that help predict future trends, illuminate previously hidden issues that can be applied to practice, and/or provide a means for understanding an important research problem with greater clarity. A case study research paper usually examines a single subject of analysis, but case study papers can also be designed as a comparative investigation that shows relationships between two or more subjects. The methods used to study a case can rest within a quantitative, qualitative, or mixed-method investigative paradigm.

Case Studies. Writing@CSU. Colorado State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010 ; “What is a Case Study?” In Swanborn, Peter G. Case Study Research: What, Why and How? London: SAGE, 2010.

How to Approach Writing a Case Study Research Paper

General information about how to choose a topic to investigate can be found under the " Choosing a Research Problem " tab in the Organizing Your Social Sciences Research Paper writing guide. Review this page because it may help you identify a subject of analysis that can be investigated using a case study design.

However, identifying a case to investigate involves more than choosing the research problem . A case study encompasses a problem contextualized around the application of in-depth analysis, interpretation, and discussion, often resulting in specific recommendations for action or for improving existing conditions. As Seawright and Gerring note, practical considerations such as time and access to information can influence case selection, but these issues should not be the sole factors used in describing the methodological justification for identifying a particular case to study. Given this, selecting a case includes considering the following:

• The case represents an unusual or atypical example of a research problem that requires more in-depth analysis? Cases often represent a topic that rests on the fringes of prior investigations because the case may provide new ways of understanding the research problem. For example, if the research problem is to identify strategies to improve policies that support girl's access to secondary education in predominantly Muslim nations, you could consider using Azerbaijan as a case study rather than selecting a more obvious nation in the Middle East. Doing so may reveal important new insights into recommending how governments in other predominantly Muslim nations can formulate policies that support improved access to education for girls.
• The case provides important insight or illuminate a previously hidden problem? In-depth analysis of a case can be based on the hypothesis that the case study will reveal trends or issues that have not been exposed in prior research or will reveal new and important implications for practice. For example, anecdotal evidence may suggest drug use among homeless veterans is related to their patterns of travel throughout the day. Assuming prior studies have not looked at individual travel choices as a way to study access to illicit drug use, a case study that observes a homeless veteran could reveal how issues of personal mobility choices facilitate regular access to illicit drugs. Note that it is important to conduct a thorough literature review to ensure that your assumption about the need to reveal new insights or previously hidden problems is valid and evidence-based.
• The case challenges and offers a counter-point to prevailing assumptions? Over time, research on any given topic can fall into a trap of developing assumptions based on outdated studies that are still applied to new or changing conditions or the idea that something should simply be accepted as "common sense," even though the issue has not been thoroughly tested in current practice. A case study analysis may offer an opportunity to gather evidence that challenges prevailing assumptions about a research problem and provide a new set of recommendations applied to practice that have not been tested previously. For example, perhaps there has been a long practice among scholars to apply a particular theory in explaining the relationship between two subjects of analysis. Your case could challenge this assumption by applying an innovative theoretical framework [perhaps borrowed from another discipline] to explore whether this approach offers new ways of understanding the research problem. Taking a contrarian stance is one of the most important ways that new knowledge and understanding develops from existing literature.
• The case provides an opportunity to pursue action leading to the resolution of a problem? Another way to think about choosing a case to study is to consider how the results from investigating a particular case may result in findings that reveal ways in which to resolve an existing or emerging problem. For example, studying the case of an unforeseen incident, such as a fatal accident at a railroad crossing, can reveal hidden issues that could be applied to preventative measures that contribute to reducing the chance of accidents in the future. In this example, a case study investigating the accident could lead to a better understanding of where to strategically locate additional signals at other railroad crossings so as to better warn drivers of an approaching train, particularly when visibility is hindered by heavy rain, fog, or at night.
• The case offers a new direction in future research? A case study can be used as a tool for an exploratory investigation that highlights the need for further research about the problem. A case can be used when there are few studies that help predict an outcome or that establish a clear understanding about how best to proceed in addressing a problem. For example, after conducting a thorough literature review [very important!], you discover that little research exists showing the ways in which women contribute to promoting water conservation in rural communities of east central Africa. A case study of how women contribute to saving water in a rural village of Uganda can lay the foundation for understanding the need for more thorough research that documents how women in their roles as cooks and family caregivers think about water as a valuable resource within their community. This example of a case study could also point to the need for scholars to build new theoretical frameworks around the topic [e.g., applying feminist theories of work and family to the issue of water conservation].

Eisenhardt, Kathleen M. “Building Theories from Case Study Research.” Academy of Management Review 14 (October 1989): 532-550; Emmel, Nick. Sampling and Choosing Cases in Qualitative Research: A Realist Approach . Thousand Oaks, CA: SAGE Publications, 2013; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Seawright, Jason and John Gerring. "Case Selection Techniques in Case Study Research." Political Research Quarterly 61 (June 2008): 294-308.

Structure and Writing Style

The purpose of a paper in the social sciences designed around a case study is to thoroughly investigate a subject of analysis in order to reveal a new understanding about the research problem and, in so doing, contributing new knowledge to what is already known from previous studies. In applied social sciences disciplines [e.g., education, social work, public administration, etc.], case studies may also be used to reveal best practices, highlight key programs, or investigate interesting aspects of professional work.

In general, the structure of a case study research paper is not all that different from a standard college-level research paper. However, there are subtle differences you should be aware of. Here are the key elements to organizing and writing a case study research paper.

I.  Introduction

As with any research paper, your introduction should serve as a roadmap for your readers to ascertain the scope and purpose of your study . The introduction to a case study research paper, however, should not only describe the research problem and its significance, but you should also succinctly describe why the case is being used and how it relates to addressing the problem. The two elements should be linked. With this in mind, a good introduction answers these four questions:

• What is being studied? Describe the research problem and describe the subject of analysis [the case] you have chosen to address the problem. Explain how they are linked and what elements of the case will help to expand knowledge and understanding about the problem.
• Why is this topic important to investigate? Describe the significance of the research problem and state why a case study design and the subject of analysis that the paper is designed around is appropriate in addressing the problem.
• What did we know about this topic before I did this study? Provide background that helps lead the reader into the more in-depth literature review to follow. If applicable, summarize prior case study research applied to the research problem and why it fails to adequately address the problem. Describe why your case will be useful. If no prior case studies have been used to address the research problem, explain why you have selected this subject of analysis.
• How will this study advance new knowledge or new ways of understanding? Explain why your case study will be suitable in helping to expand knowledge and understanding about the research problem.

Each of these questions should be addressed in no more than a few paragraphs. Exceptions to this can be when you are addressing a complex research problem or subject of analysis that requires more in-depth background information.

II.  Literature Review

The literature review for a case study research paper is generally structured the same as it is for any college-level research paper. The difference, however, is that the literature review is focused on providing background information and  enabling historical interpretation of the subject of analysis in relation to the research problem the case is intended to address . This includes synthesizing studies that help to:

• Place relevant works in the context of their contribution to understanding the case study being investigated . This would involve summarizing studies that have used a similar subject of analysis to investigate the research problem. If there is literature using the same or a very similar case to study, you need to explain why duplicating past research is important [e.g., conditions have changed; prior studies were conducted long ago, etc.].
• Describe the relationship each work has to the others under consideration that informs the reader why this case is applicable . Your literature review should include a description of any works that support using the case to investigate the research problem and the underlying research questions.
• Identify new ways to interpret prior research using the case study . If applicable, review any research that has examined the research problem using a different research design. Explain how your use of a case study design may reveal new knowledge or a new perspective or that can redirect research in an important new direction.
• Resolve conflicts amongst seemingly contradictory previous studies . This refers to synthesizing any literature that points to unresolved issues of concern about the research problem and describing how the subject of analysis that forms the case study can help resolve these existing contradictions.
• Point the way in fulfilling a need for additional research . Your review should examine any literature that lays a foundation for understanding why your case study design and the subject of analysis around which you have designed your study may reveal a new way of approaching the research problem or offer a perspective that points to the need for additional research.
• Expose any gaps that exist in the literature that the case study could help to fill . Summarize any literature that not only shows how your subject of analysis contributes to understanding the research problem, but how your case contributes to a new way of understanding the problem that prior research has failed to do.
• Locate your own research within the context of existing literature [very important!] . Collectively, your literature review should always place your case study within the larger domain of prior research about the problem. The overarching purpose of reviewing pertinent literature in a case study paper is to demonstrate that you have thoroughly identified and synthesized prior studies in relation to explaining the relevance of the case in addressing the research problem.

III.  Method

In this section, you explain why you selected a particular case [i.e., subject of analysis] and the strategy you used to identify and ultimately decide that your case was appropriate in addressing the research problem. The way you describe the methods used varies depending on the type of subject of analysis that constitutes your case study.

If your subject of analysis is an incident or event . In the social and behavioral sciences, the event or incident that represents the case to be studied is usually bounded by time and place, with a clear beginning and end and with an identifiable location or position relative to its surroundings. The subject of analysis can be a rare or critical event or it can focus on a typical or regular event. The purpose of studying a rare event is to illuminate new ways of thinking about the broader research problem or to test a hypothesis. Critical incident case studies must describe the method by which you identified the event and explain the process by which you determined the validity of this case to inform broader perspectives about the research problem or to reveal new findings. However, the event does not have to be a rare or uniquely significant to support new thinking about the research problem or to challenge an existing hypothesis. For example, Walo, Bull, and Breen conducted a case study to identify and evaluate the direct and indirect economic benefits and costs of a local sports event in the City of Lismore, New South Wales, Australia. The purpose of their study was to provide new insights from measuring the impact of a typical local sports event that prior studies could not measure well because they focused on large "mega-events." Whether the event is rare or not, the methods section should include an explanation of the following characteristics of the event: a) when did it take place; b) what were the underlying circumstances leading to the event; and, c) what were the consequences of the event in relation to the research problem.

If your subject of analysis is a person. Explain why you selected this particular individual to be studied and describe what experiences they have had that provide an opportunity to advance new understandings about the research problem. Mention any background about this person which might help the reader understand the significance of their experiences that make them worthy of study. This includes describing the relationships this person has had with other people, institutions, and/or events that support using them as the subject for a case study research paper. It is particularly important to differentiate the person as the subject of analysis from others and to succinctly explain how the person relates to examining the research problem [e.g., why is one politician in a particular local election used to show an increase in voter turnout from any other candidate running in the election]. Note that these issues apply to a specific group of people used as a case study unit of analysis [e.g., a classroom of students].

If your subject of analysis is a place. In general, a case study that investigates a place suggests a subject of analysis that is unique or special in some way and that this uniqueness can be used to build new understanding or knowledge about the research problem. A case study of a place must not only describe its various attributes relevant to the research problem [e.g., physical, social, historical, cultural, economic, political], but you must state the method by which you determined that this place will illuminate new understandings about the research problem. It is also important to articulate why a particular place as the case for study is being used if similar places also exist [i.e., if you are studying patterns of homeless encampments of veterans in open spaces, explain why you are studying Echo Park in Los Angeles rather than Griffith Park?]. If applicable, describe what type of human activity involving this place makes it a good choice to study [e.g., prior research suggests Echo Park has more homeless veterans].

If your subject of analysis is a phenomenon. A phenomenon refers to a fact, occurrence, or circumstance that can be studied or observed but with the cause or explanation to be in question. In this sense, a phenomenon that forms your subject of analysis can encompass anything that can be observed or presumed to exist but is not fully understood. In the social and behavioral sciences, the case usually focuses on human interaction within a complex physical, social, economic, cultural, or political system. For example, the phenomenon could be the observation that many vehicles used by ISIS fighters are small trucks with English language advertisements on them. The research problem could be that ISIS fighters are difficult to combat because they are highly mobile. The research questions could be how and by what means are these vehicles used by ISIS being supplied to the militants and how might supply lines to these vehicles be cut off? How might knowing the suppliers of these trucks reveal larger networks of collaborators and financial support? A case study of a phenomenon most often encompasses an in-depth analysis of a cause and effect that is grounded in an interactive relationship between people and their environment in some way.

NOTE:   The choice of the case or set of cases to study cannot appear random. Evidence that supports the method by which you identified and chose your subject of analysis should clearly support investigation of the research problem and linked to key findings from your literature review. Be sure to cite any studies that helped you determine that the case you chose was appropriate for examining the problem.

IV.  Discussion

The main elements of your discussion section are generally the same as any research paper, but centered around interpreting and drawing conclusions about the key findings from your analysis of the case study. Note that a general social sciences research paper may contain a separate section to report findings. However, in a paper designed around a case study, it is common to combine a description of the results with the discussion about their implications. The objectives of your discussion section should include the following:

Reiterate the Research Problem/State the Major Findings Briefly reiterate the research problem you are investigating and explain why the subject of analysis around which you designed the case study were used. You should then describe the findings revealed from your study of the case using direct, declarative, and succinct proclamation of the study results. Highlight any findings that were unexpected or especially profound.

Explain the Meaning of the Findings and Why They are Important Systematically explain the meaning of your case study findings and why you believe they are important. Begin this part of the section by repeating what you consider to be your most important or surprising finding first, then systematically review each finding. Be sure to thoroughly extrapolate what your analysis of the case can tell the reader about situations or conditions beyond the actual case that was studied while, at the same time, being careful not to misconstrue or conflate a finding that undermines the external validity of your conclusions.

Relate the Findings to Similar Studies No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your case study results to those found in other studies, particularly if questions raised from prior studies served as the motivation for choosing your subject of analysis. This is important because comparing and contrasting the findings of other studies helps support the overall importance of your results and it highlights how and in what ways your case study design and the subject of analysis differs from prior research about the topic.

Consider Alternative Explanations of the Findings Remember that the purpose of social science research is to discover and not to prove. When writing the discussion section, you should carefully consider all possible explanations revealed by the case study results, rather than just those that fit your hypothesis or prior assumptions and biases. Be alert to what the in-depth analysis of the case may reveal about the research problem, including offering a contrarian perspective to what scholars have stated in prior research if that is how the findings can be interpreted from your case.

Acknowledge the Study's Limitations You can state the study's limitations in the conclusion section of your paper but describing the limitations of your subject of analysis in the discussion section provides an opportunity to identify the limitations and explain why they are not significant. This part of the discussion section should also note any unanswered questions or issues your case study could not address. More detailed information about how to document any limitations to your research can be found here .

Suggest Areas for Further Research Although your case study may offer important insights about the research problem, there are likely additional questions related to the problem that remain unanswered or findings that unexpectedly revealed themselves as a result of your in-depth analysis of the case. Be sure that the recommendations for further research are linked to the research problem and that you explain why your recommendations are valid in other contexts and based on the original assumptions of your study.

V.  Conclusion

As with any research paper, you should summarize your conclusion in clear, simple language; emphasize how the findings from your case study differs from or supports prior research and why. Do not simply reiterate the discussion section. Provide a synthesis of key findings presented in the paper to show how these converge to address the research problem. If you haven't already done so in the discussion section, be sure to document the limitations of your case study and any need for further research.

The function of your paper's conclusion is to: 1) reiterate the main argument supported by the findings from your case study; 2) state clearly the context, background, and necessity of pursuing the research problem using a case study design in relation to an issue, controversy, or a gap found from reviewing the literature; and, 3) provide a place to persuasively and succinctly restate the significance of your research problem, given that the reader has now been presented with in-depth information about the topic.

Consider the following points to help ensure your conclusion is appropriate:

• If the argument or purpose of your paper is complex, you may need to summarize these points for your reader.
• If prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the conclusion of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration of the case study's findings that returns the topic to the context provided by the introduction or within a new context that emerges from your case study findings.

Note that, depending on the discipline you are writing in or the preferences of your professor, the concluding paragraph may contain your final reflections on the evidence presented as it applies to practice or on the essay's central research problem. However, the nature of being introspective about the subject of analysis you have investigated will depend on whether you are explicitly asked to express your observations in this way.

Problems to Avoid

Overgeneralization One of the goals of a case study is to lay a foundation for understanding broader trends and issues applied to similar circumstances. However, be careful when drawing conclusions from your case study. They must be evidence-based and grounded in the results of the study; otherwise, it is merely speculation. Looking at a prior example, it would be incorrect to state that a factor in improving girls access to education in Azerbaijan and the policy implications this may have for improving access in other Muslim nations is due to girls access to social media if there is no documentary evidence from your case study to indicate this. There may be anecdotal evidence that retention rates were better for girls who were engaged with social media, but this observation would only point to the need for further research and would not be a definitive finding if this was not a part of your original research agenda.

Failure to Document Limitations No case is going to reveal all that needs to be understood about a research problem. Therefore, just as you have to clearly state the limitations of a general research study , you must describe the specific limitations inherent in the subject of analysis. For example, the case of studying how women conceptualize the need for water conservation in a village in Uganda could have limited application in other cultural contexts or in areas where fresh water from rivers or lakes is plentiful and, therefore, conservation is understood more in terms of managing access rather than preserving access to a scarce resource.

Failure to Extrapolate All Possible Implications Just as you don't want to over-generalize from your case study findings, you also have to be thorough in the consideration of all possible outcomes or recommendations derived from your findings. If you do not, your reader may question the validity of your analysis, particularly if you failed to document an obvious outcome from your case study research. For example, in the case of studying the accident at the railroad crossing to evaluate where and what types of warning signals should be located, you failed to take into consideration speed limit signage as well as warning signals. When designing your case study, be sure you have thoroughly addressed all aspects of the problem and do not leave gaps in your analysis that leave the reader questioning the results.

Case Studies. Writing@CSU. Colorado State University; Gerring, John. Case Study Research: Principles and Practices . New York: Cambridge University Press, 2007; Merriam, Sharan B. Qualitative Research and Case Study Applications in Education . Rev. ed. San Francisco, CA: Jossey-Bass, 1998; Miller, Lisa L. “The Use of Case Studies in Law and Social Science Research.” Annual Review of Law and Social Science 14 (2018): TBD; Mills, Albert J., Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Putney, LeAnn Grogan. "Case Study." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE Publications, 2010), pp. 116-120; Simons, Helen. Case Study Research in Practice . London: SAGE Publications, 2009;  Kratochwill,  Thomas R. and Joel R. Levin, editors. Single-Case Research Design and Analysis: New Development for Psychology and Education .  Hilldsale, NJ: Lawrence Erlbaum Associates, 1992; Swanborn, Peter G. Case Study Research: What, Why and How? London : SAGE, 2010; Yin, Robert K. Case Study Research: Design and Methods . 6th edition. Los Angeles, CA, SAGE Publications, 2014; Walo, Maree, Adrian Bull, and Helen Breen. “Achieving Economic Benefits at Local Events: A Case Study of a Local Sports Event.” Festival Management and Event Tourism 4 (1996): 95-106.

Writing Tip

At Least Five Misconceptions about Case Study Research

Social science case studies are often perceived as limited in their ability to create new knowledge because they are not randomly selected and findings cannot be generalized to larger populations. Flyvbjerg examines five misunderstandings about case study research and systematically "corrects" each one. To quote, these are:

Misunderstanding 1 :  General, theoretical [context-independent] knowledge is more valuable than concrete, practical [context-dependent] knowledge. Misunderstanding 2 :  One cannot generalize on the basis of an individual case; therefore, the case study cannot contribute to scientific development. Misunderstanding 3 :  The case study is most useful for generating hypotheses; that is, in the first stage of a total research process, whereas other methods are more suitable for hypotheses testing and theory building. Misunderstanding 4 :  The case study contains a bias toward verification, that is, a tendency to confirm the researcher’s preconceived notions. Misunderstanding 5 :  It is often difficult to summarize and develop general propositions and theories on the basis of specific case studies [p. 221].

While writing your paper, think introspectively about how you addressed these misconceptions because to do so can help you strengthen the validity and reliability of your research by clarifying issues of case selection, the testing and challenging of existing assumptions, the interpretation of key findings, and the summation of case outcomes. Think of a case study research paper as a complete, in-depth narrative about the specific properties and key characteristics of your subject of analysis applied to the research problem.

Flyvbjerg, Bent. “Five Misunderstandings About Case-Study Research.” Qualitative Inquiry 12 (April 2006): 219-245.

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The case study approach

• Sarah Crowe 1 ,
• Kathrin Cresswell 2 ,
• Ann Robertson 2 ,
• Guro Huby 3 ,
• Anthony Avery 1 &
• Aziz Sheikh 2  

BMC Medical Research Methodology volume  11 , Article number:  100 ( 2011 ) Cite this article

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The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

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Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

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Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.

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A Quick Guide to Case Study with Examples

Published by Alvin Nicolas at August 14th, 2021 , Revised On August 29, 2023

A case study is a documented history and detailed analysis of a situation concerning organisations, industries, and markets.

A case study:

• Focuses on discovering new facts of the situation under observation.
• Includes data collection from multiple sources over time.
• Widely used in social sciences to study the underlying information, organisation, community, or event.
• It does not provide any solution to the problem .

When to Use Case Study? 

You can use a case study in your research when:

• The focus of your study is to find answers to how and why questions .
• You don’t have enough time to conduct extensive research; case studies are convenient for completing your project successfully.
• You want to analyse real-world problems in-depth, then you can use the method of the case study.

You can consider a single case to gain in-depth knowledge about the subject, or you can choose multiple cases to know about various aspects of your  research problem .

What are the Aims of the Case Study?

• The case study aims at identifying weak areas that can be improved.
• This method is often used for idiographic research (focuses on individual cases or events).
• Another aim of the case study is nomothetic research (aims to discover new theories through data analysis of multiple cases).

Types of Case Studies

There are different types of case studies that can be categorised based on the purpose of the investigation.

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How to Conduct a Case Study?

• Select the Case to Investigate
• Formulate the Research Question
• Review of Literature
• Choose the Precise Case to Use in your Study
• Select Data Collection and Analysis Techniques
• Collect the Data
• Analyse the Data
• Prepare the Report

Step1: Select the Case to Investigate

The first step is to select a case to conduct your investigation. You should remember the following points.

• Make sure that you perform the study in the available timeframe.
• There should not be too much information available about the organisation.
• You should be able to get access to the organisation.
• There should be enough information available about the subject to conduct further research.

Step2: Formulate the Research Question

It’s necessary to  formulate a research question  to proceed with your case study. Most of the research questions begin with  how, why, what, or what can . 

You can also use a research statement instead of a research question to conduct your research which can be conditional or non-conditional. 

Step 3: Review of Literature

Once you formulate your research statement or question, you need to extensively  review the documentation about the existing discoveries related to your research question or statement.

Step 4: Choose the Precise Case to Use in your Study

You need to select a specific case or multiple cases related to your research. It would help if you treated each case individually while using multiple cases. The outcomes of each case can be used as contributors to the outcomes of the entire study.  You can select the following cases. 

• Representing various geographic regions
• Cases with various size parameters
• Explaining the existing theories or assumptions
• Leading to discoveries
• Providing a base for future research.

Step 5: Select Data Collection and Analysis Techniques

You can choose both  qualitative or quantitative approaches  for  collecting the data . You can use  interviews ,  surveys , artifacts, documentation, newspapers, and photographs, etc. To avoid biased observation, you can triangulate  your research to provide different views of your case. Even if you are focusing on a single case, you need to observe various case angles. It would help if you constructed validity, internal and external validity, as well as reliability.

Example: Identifying the impacts of contaminated water on people’s health and the factors responsible for it. You need to gather the data using qualitative and quantitative approaches to understand the case in such cases.

Construct validity:  You should select the most suitable measurement tool for your research. 

Internal validity:   You should use various methodological tools to  triangulate  the data. Try different methods to study the same hypothesis.

External validity:  You need to effectively apply the data beyond the case’s circumstances to more general issues.

Reliability:   You need to be confident enough to formulate the new direction for future studies based on your findings.

Also Read:  Reliability and Validity

Step 6: Collect the Data

Beware of the following when collecting data:

• Information should be gathered systematically, and the collected evidence from various sources should contribute to your research objectives.
• Don’t collect your data randomly.
• Recheck your research questions to avoid mistakes.
• You should save the collected data in any popular format for clear understanding.
• While making any changes to collecting information, make sure to record the changes in a document.
• You should maintain a case diary and note your opinions and thoughts evolved throughout the study.

Step 7: Analyse the Data

The research data identifies the relationship between the objects of study and the research questions or statements. You need to reconfirm the collected information and tabulate it correctly for better understanding. 

Step 8: Prepare the Report

It’s essential to prepare a report for your case study. You can write your case study in the form of a scientific paper or thesis discussing its detail with supporting evidence. 

A case study can be represented by incorporating  quotations,  stories, anecdotes,  interview transcripts , etc., with empirical data in the result section. 

You can also write it in narrative styles using  textual analysis  or   discourse analysis . Your report should also include evidence from published literature, and you can put it in the discussion section.

Advantages and Disadvantages of Case Study

Frequently Asked Questions

What is the case study.

A case study is a research method where a specific instance, event, or situation is deeply examined to gain insights into real-world complexities. It involves detailed analysis of context, data, and variables to understand patterns, causes, and effects, often used in various disciplines for in-depth exploration.

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Case Study Research Method in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head.  Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868).  Recovery from the Passage of an Iron Bar through the Head .  Publications of the Massachusetts Medical Society. 2  (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972).  Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

• Case Study Approach
• Case Study Method
• Enhancing the Quality of Case Studies in Health Services Research
• “We do things together” A case study of “couplehood” in dementia
• Using mixed methods for evaluating an integrative approach to cancer care: a case study

What Is The Purpose Of A Case Study?

A case study serves the purpose of deeply examining a specific subject or phenomenon within its real-life context. By meticulously analyzing a single case in detail, researchers aim to gain a comprehensive understanding of the complexities, nuances, and dynamics involved in the subject matter under investigation. Case studies offer a unique opportunity to explore and elucidate how various factors interact and influence outcomes, providing valuable insights that can inform theories, practices, and decision-making processes within academic disciplines and professional fields alike. They offer a means to investigate rare or exceptional situations, shed light on causal processes, and generate rich empirical data, all of which contribute to the advancement of knowledge and understanding.

Table of Contents

Definition of a case study

A case study is a research strategy that involves an in-depth examination of a particular individual, group, organization, or event. It aims to provide comprehensive and detailed insights into the chosen subject of study, delving into the complexities of real-life situations. A case study typically involves multiple data sources, including interviews, observations, documents, and other relevant materials. The findings of a case study can be used to generate insights, develop solutions, and inform decision-making processes.

Importance of case studies

Case studies hold significant importance in various academic fields, including psychology, sociology, business, and medicine, among others. They provide researchers with a unique opportunity to explore complex phenomena in their natural settings, allowing for a thorough understanding of real-life situations. Furthermore, case studies enable researchers to test and refine theories, explore new perspectives, and generate knowledge that can be applied in practical contexts. They also help bridge the gap between theory and practice, offering practitioners valuable insights and lessons learned.

Understanding the Problem

Identifying the research question.

Before embarking on a case study, it is crucial to identify a clear research question that will guide the investigation. The research question should be specific, focused, and relevant to the field of study. It should address an existing knowledge gap or seek to uncover new insights. The research question will serve as a compass throughout the case study, ensuring that the investigation remains focused and coherent.

Exploring the background and context

To fully understand the problem at hand, it is essential to explore the background and context surrounding the chosen case study subject. This involves gathering information about the historical, social, economic, and cultural factors that may influence the subject. By comprehensively examining the context, researchers can gain a deeper understanding of the complexities and dynamics involved, allowing for a more nuanced analysis of the case.

Research Design

Choosing the case study approach.

When designing a case study, researchers must choose the appropriate approach that aligns with the research question and objectives. There are several types of case study approaches, including exploratory, explanatory, and descriptive. Exploratory case studies aim to generate hypotheses and explore new areas of research. Explanatory case studies seek to determine cause-and-effect relationships. Descriptive case studies aim to provide a detailed account of a specific phenomenon. The chosen approach will shape the overall research design and methodology.

Selecting the appropriate case

Selecting the appropriate case for study is a critical decision that impacts the validity and generalizability of the findings. Researchers must consider various factors when selecting a case, such as relevance, uniqueness, and feasibility. The case should be relevant to the research question and offer valuable insights into the phenomena of interest. It should also possess unique characteristics or features that make it worthy of investigation. Additionally, the feasibility of accessing data and conducting the study should be carefully evaluated.

Data Collection

Determining data sources.

Data sources play a crucial role in case study research. These sources can include interviews, observations, documents, archival records, and other relevant materials. Typically, a combination of primary and secondary data sources is used to provide a comprehensive understanding of the case. Primary data sources involve firsthand information collected directly from participants or through direct observations. Secondary data sources involve pre-existing information that is analyzed in relation to the case study.

Collecting primary data

Collecting primary data involves engaging with participants or observing the case firsthand. This can be achieved through various methods, such as interviews, focus groups, surveys, or participant observation. Interviews allow researchers to gather detailed information and explore participants’ perspectives, experiences, and motivations. Focus groups provide a platform for participants to engage in group discussions and share insights. Surveys offer a structured way to collect data from a larger sample. Participant observation involves immersing oneself in the case study environment to directly observe and record behaviors and interactions.

Gathering secondary data

Secondary data sources complement primary data and enhance the richness of the case study. These sources include existing documents, archival records, scholarly articles, industry reports, and other relevant materials. Researchers must carefully select and analyze secondary data, ensuring it aligns with the research question and complements the primary data. A thorough examination of secondary data can contribute to a comprehensive understanding of the case and provide historical or background contextual information.

Data Analysis

Applying data analysis techniques.

Data analysis is a crucial step in case study research and involves transforming raw data into meaningful insights. Various data analysis techniques can be employed, including thematic analysis, content analysis, narrative analysis, and statistical analysis, among others. Thematic analysis involves identifying and categorizing themes or patterns within the data. Content analysis focuses on identifying and analyzing specific words, phrases, or concepts within the data. Narrative analysis seeks to uncover the underlying stories and narratives that emerge from the data. Statistical analysis involves quantifying and analyzing numerical data.

Identifying patterns and themes

During the data analysis process, researchers must carefully examine the data to identify patterns, themes, and relationships. This involves organizing and categorizing the data based on recurring ideas, concepts, or patterns that emerge. By identifying these patterns and themes, researchers can gain insights into the relationships and dynamics present in the case study. It allows for a deeper understanding of the complexities and nuances within the data and supports the generation of meaningful conclusions.

Generating Insights

Linking findings to research question.

The findings derived from the data analysis should be linked back to the research question and objectives of the case study. It is essential to establish the relevance and significance of the findings in relation to the original research question. By establishing this link, researchers can validate the findings and ensure their alignment with the objectives of the study. This step is crucial for generating insights that contribute to the existing knowledge base and address the research question effectively.

Drawing meaningful conclusions

Drawing meaningful conclusions from the case study involves synthesizing the key findings and deriving insights from the analysis. Researchers must critically evaluate the findings, considering their strengths and limitations, and interpret them in light of the research question and relevant literature. The conclusions should be justified and supported by empirical evidence. Meaningful conclusions will contribute to a deeper understanding of the case, provide practical implications, and pave the way for further research or the development of solutions.

Developing Solutions

Identifying potential solutions.

Based on the insights generated from the case study, researchers can identify potential solutions to the problem at hand. These solutions should be grounded in empirical evidence and address the key issues identified through the research. It is crucial to consider multiple perspectives and approaches when identifying potential solutions, evaluating their feasibility, effectiveness, and ethical implications. The solutions should align with the objectives of the case study and offer practical recommendations for addressing the problem.

Evaluating feasibility and effectiveness

After identifying potential solutions, it is important to evaluate their feasibility and effectiveness. This involves considering the resources, constraints, and practical implications associated with implementing the proposed solutions. Feasibility assessment involves evaluating whether the proposed solutions can be realistically implemented within the given context, timeframe, and available resources. Effectiveness evaluation involves assessing the potential impact of the solutions and their ability to address the identified problem.

Knowledge Application

Informing decision-making.

The findings and insights derived from a case study can be instrumental in informing decision-making processes. Decision-makers can draw upon the knowledge generated through the case study to make informed choices and develop strategies. The detailed analysis of the case, combined with the empirical evidence and practical implications, provides decision-makers with valuable insights and evidence-based recommendations. By utilizing the knowledge gained from case studies, decision-makers can optimize outcomes and enhance the effectiveness of their decisions.

Sharing lessons learned

Case studies also serve as a valuable source of knowledge dissemination. Sharing the lessons learned from a case study can benefit researchers, practitioners, academics, and other stakeholders in the field. By presenting the findings, insights, and recommendations, case studies contribute to the existing knowledge base, spark further discussions, and inspire new research. Sharing lessons learned facilitates the exchange of ideas, promotes collaboration, and supports ongoing learning and development within the respective field.

Strengths and Limitations

Highlighting advantages of case studies.

Case studies offer various advantages as a research method. They provide researchers with the opportunity to explore real-life phenomena in their natural context, offering a deep understanding of complex situations. Case studies can generate rich and detailed data, allowing for in-depth analysis and insights. They also provide a holistic perspective, considering multiple factors and variables. Case studies are particularly useful for exploring complex and dynamic phenomena that cannot be easily captured through quantitative methods.

Addressing potential biases

Like any research method, case studies are not without limitations. One potential limitation is the presence of biases in the data collection and analysis process. Researchers must be aware of their own biases and take steps to minimize their influence on the findings. To address this limitation, researchers can engage in reflexivity, seeking to critically evaluate their own perspectives and assumptions throughout the research process. Additionally, triangulation, the use of multiple data sources and perspectives, can help mitigate potential biases and enhance the validity of the findings.

Promoting Further Research

Building on existing knowledge.

Case studies often uncover new areas of research and generate additional questions for further investigation. Researchers can build on existing knowledge by exploring gaps identified through the case study and proposing new research avenues. The in-depth analysis and insights gained from the case study can inform the development of hypotheses or theories, which can then be tested through quantitative research methods. By building on existing knowledge, researchers contribute to the advancement of the field and foster ongoing exploration and discovery.

Exploring new perspectives

Case studies provide an opportunity to explore new perspectives and alternative approaches to understanding a phenomenon. Researchers can use the detailed analysis and insights gained from a case study to challenge existing theories or assumptions and propose new perspectives. This exploration of new perspectives can lead to innovative insights and alternative explanations for complex phenomena. By embracing diverse perspectives and exploring new avenues, researchers can push the boundaries of knowledge and stimulate new lines of inquiry.

In conclusion, case studies serve as a valuable research strategy for gaining an in-depth understanding of complex phenomena. By employing a systematic approach for each stage of the case study process, researchers can ensure rigor, validity, and relevance to the research question. Case studies have the potential to generate rich insights, inform decision-making, and contribute to the existing knowledge base within various academic fields. However, it is important to acknowledge the strengths and limitations of case studies and continually strive to promote further research and exploration of new perspectives.

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Blog Beginner Guides 6 Types of Case Studies to Inspire Your Research and Analysis

6 Types of Case Studies to Inspire Your Research and Analysis

Written by: Ronita Mohan Sep 20, 2021

Case studies have become powerful business tools. But what is a case study? What are the benefits of creating one? Are there limitations to the format?

If you’ve asked yourself these questions, our helpful guide will clear things up. Learn how to use a case study for business. Find out how cases analysis works in psychology and research.

We’ve also got examples of case studies to inspire you.

Haven’t made a case study before? You can easily  create a case study  with Venngage’s customizable case study templates .

Click to jump ahead:

What is a case study?

6 types of case studies, what is a business case study, what is a case study in research, what is a case study in psychology, what is the case study method, benefits of case studies, limitations of case studies, faqs about case studies.

A case study is a research process aimed at learning about a subject, an event or an organization. Case studies are use in business, the social sciences and healthcare.

A case study may focus on one observation or many. It can also examine a series of events or a single case. An effective case study tells a story and provides a conclusion.

Healthcare industries write reports on patients and diagnoses. Marketing case study examples , like the one below, highlight the benefits of a business product.

Now that you know what a case study is, let’s look at the six different types of case studies next.

There are six common types of case reports. Depending on your industry, you might use one of these types.

Descriptive case studies

Explanatory case studies, exploratory case reports, intrinsic case studies, instrumental case studies, collective case reports.

We go into more detail about each type of study in the guide below.

Related:  15+ Professional Case Study Examples [Design Tips + Templates]

When you have an existing hypothesis, you can design a descriptive study. This type of report starts with a description. The aim is to find connections between the subject being studied and a theory.

Once these connections are found, the study can conclude. The results of this type of study will usually suggest how to develop a theory further.

A study like the one below has concrete results. A descriptive report would use the quantitative data as a suggestion for researching the subject deeply.

When an incident occurs in a field, an explanation is required. An explanatory report investigates the cause of the event. It will include explanations for that cause.

The study will also share details about the impact of the event. In most cases, this report will use evidence to predict future occurrences. The results of explanatory reports are definitive.

Note that there is no room for interpretation here. The results are absolute.

The study below is a good example. It explains how one brand used the services of another. It concludes by showing definitive proof that the collaboration was successful.

Another example of this study would be in the automotive industry. If a vehicle fails a test, an explanatory study will examine why. The results could show that the failure was because of a particular part.

Related: How to Write a Case Study [+ Design Tips]

An explanatory report is a self-contained document. An exploratory one is only the beginning of an investigation.

Exploratory cases act as the starting point of studies. This is usually conducted as a precursor to large-scale investigations. The research is used to suggest why further investigations are needed.

An exploratory study can also be used to suggest methods for further examination.

For example, the below analysis could have found inconclusive results. In that situation, it would be the basis for an in-depth study.

Intrinsic studies are more common in the field of psychology. These reports can also be conducted in healthcare or social work.

These types of studies focus on a unique subject, such as a patient. They can sometimes study groups close to the researcher.

The aim of such studies is to understand the subject better. This requires learning their history. The researcher will also examine how they interact with their environment.

For instance, if the case study below was about a unique brand, it could be an intrinsic study.

Once the study is complete, the researcher will have developed a better understanding of a phenomenon. This phenomenon will likely not have been studied or theorized about before.

Examples of intrinsic case analysis can be found across psychology. For example, Jean Piaget’s theories on cognitive development. He established the theory from intrinsic studies into his own children.

Related: What Disney Villains Can Tell Us About Color Psychology [Infographic]

This is another type of study seen in medical and psychology fields. Instrumental reports are created to examine more than just the primary subject.

When research is conducted for an instrumental study, it is to provide the basis for a larger phenomenon. The subject matter is usually the best example of the phenomenon. This is why it is being studied.

Take the example of the fictional brand below.

Assume it’s examining lead generation strategies. It may want to show that visual marketing is the definitive lead generation tool. The brand can conduct an instrumental case study to examine this phenomenon.

Collective studies are based on instrumental case reports. These types of studies examine multiple reports.

There are a number of reasons why collective reports are created:

• To provide evidence for starting a new study
• To find pattens between multiple instrumental reports
• To find differences in similar types of cases
• Gain a deeper understanding of a complex phenomenon
• Understand a phenomenon from diverse contexts

A researcher could use multiple reports, like the one below, to build a collective case report.

Related: 10+ Case Study Infographic Templates That Convert

A business or marketing case study aims at showcasing a successful partnership. This can be between a brand and a client. Or the case study can examine a brand’s project.

There is a perception that case studies are used to advertise a brand. But effective reports, like the one below, can show clients how a brand can support them.

Hubspot created a case study on a customer that successfully scaled its business. The report outlines the various Hubspot tools used to achieve these results.

Hubspot also added a video with testimonials from the client company’s employees.

So, what is the purpose of a case study for businesses? There is a lot of competition in the corporate world. Companies are run by people. They can be on the fence about which brand to work with.

Business reports  stand out aesthetically, as well. They use  brand colors  and brand fonts . Usually, a combination of the client’s and the brand’s.

With the Venngage  My Brand Kit  feature, businesses can automatically apply their brand to designs.

A business case study, like the one below, acts as social proof. This helps customers decide between your brand and your competitors.

Don’t know how to design a report? You can learn  how to write a case study  with Venngage’s guide. We also share design tips and examples that will help you convert.

Related: 55+ Annual Report Design Templates, Inspirational Examples & Tips [Updated]

Research is a necessary part of every case study. But specific research fields are required to create studies. These fields include user research, healthcare, education, or social work.

For example, this UX Design  report examined the public perception of a client. The brand researched and implemented new visuals to improve it. The study breaks down this research through lessons learned.

Clinical reports are a necessity in the medical field. These documents are used to share knowledge with other professionals. They also help examine new or unusual diseases or symptoms.

The pandemic has led to a significant increase in research. For example,  Spectrum Health  studied the value of health systems in the pandemic. They created the study by examining community outreach.

The pandemic has significantly impacted the field of education. This has led to numerous examinations on remote studying. There have also been studies on how students react to decreased peer communication.

Social work case reports often have a community focus. They can also examine public health responses. In certain regions, social workers study disaster responses.

You now know what case studies in various fields are. In the next step of our guide, we explain the case study method.

In the field of psychology, case studies focus on a particular subject. Psychology case histories also examine human behaviors.

Case reports search for commonalities between humans. They are also used to prescribe further research. Or these studies can elaborate on a solution for a behavioral ailment.

The American Psychology Association  has a number of case studies on real-life clients. Note how the reports are more text-heavy than a business case study.

Famous psychologists such as Sigmund Freud and Anna O popularised the use of case studies in the field. They did so by regularly interviewing subjects. Their detailed observations build the field of psychology.

It is important to note that psychological studies must be conducted by professionals. Psychologists, psychiatrists and therapists should be the researchers in these cases.

Related: What Netflix’s Top 50 Shows Can Teach Us About Font Psychology [Infographic]

The case study method, or case method, is a learning technique where you’re presented with a real-world business challenge and asked how you’d solve it.

After working through it independently and with peers, you learn how the actual scenario unfolded. This approach helps develop problem-solving skills and practical knowledge.

This method often uses various data sources like interviews, observations, and documents to provide comprehensive insights. The below example would have been created after numerous interviews.

Case studies are largely qualitative. They analyze and describe phenomena. While some data is included, a case analysis is not quantitative.

There are a few steps in the case method. You have to start by identifying the subject of your study. Then determine what kind of research is required.

In natural sciences, case studies can take years to complete. Business reports, like this one, don’t take that long. A few weeks of interviews should be enough.

The case method will vary depending on the industry. Reports will also look different once produced.

As you will have seen, business reports are more colorful. The design is also more accessible . Healthcare and psychology reports are more text-heavy.

Designing case reports takes time and energy. So, is it worth taking the time to write them? Here are the benefits of creating case studies.

• Collects large amounts of information
• Helps formulate hypotheses
• Builds the case for further research
• Discovers new insights into a subject
• Builds brand trust and loyalty
• Engages customers through stories

For example, the business study below creates a story around a brand partnership. It makes for engaging reading. The study also shows evidence backing up the information.

We’ve shared the benefits of why studies are needed. We will also look at the limitations of creating them.

Related: How to Present a Case Study like a Pro (With Examples)

There are a few disadvantages to conducting a case analysis. The limitations will vary according to the industry.

• Responses from interviews are subjective
• Subjects may tailor responses to the researcher
• Studies can’t always be replicated
• In certain industries, analyses can take time and be expensive
• Risk of generalizing the results among a larger population

These are some of the common weaknesses of creating case reports. If you’re on the fence, look at the competition in your industry.

Other brands or professionals are building reports, like this example. In that case, you may want to do the same.

What makes a case study a case study?

A case study has a very particular research methodology. They are an in-depth study of a person or a group of individuals. They can also study a community or an organization. Case reports examine real-world phenomena within a set context.

How long should a case study be?

The length of studies depends on the industry. It also depends on the story you’re telling. Most case studies should be at least 500-1500 words long. But you can increase the length if you have more details to share.

What should you ask in a case study?

The one thing you shouldn’t ask is ‘yes’ or ‘no’ questions. Case studies are qualitative. These questions won’t give you the information you need.

Ask your client about the problems they faced. Ask them about solutions they found. Or what they think is the ideal solution. Leave room to ask them follow-up questions. This will help build out the study.

How to present a case study?

When you’re ready to present a case study, begin by providing a summary of the problem or challenge you were addressing. Follow this with an outline of the solution you implemented, and support this with the results you achieved, backed by relevant data. Incorporate visual aids like slides, graphs, and images to make your case study presentation more engaging and impactful.

Now you know what a case study means, you can begin creating one. These reports are a great tool for analyzing brands. They are also useful in a variety of other fields.

Use a visual communication platform like Venngage to design case studies. With Venngage’s templates, you can design easily. Create branded, engaging reports, all without design experience.

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• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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The Main Purpose of Case Studies and How to Effectively Hit the Target

Case studies generally have the following structure: it starts with a problem, then, outlines the various different potential solutions available. This is, then, followed by proven cases where one of the solutions discussed resulted in a company, organization or individual solving the problem outlined. The main purpose of case studies, therefore, is to find a real-life application of a theoretical concept or solution. Most of the time, problems are or can be solved theoretically.

The actual implementation of the solution, however, can often be an unpredictable event. The world today is very volatile, and what is true today might not necessarily be true the next day. For any solution to be trusted, it is important to prove that it can be translated from the books or classroom to the field and that it can, then, work effectively when this happens.

How to Effectively Hit the Target

However, for the case study to be truly useful, one must select the right case for study. The first step when choosing a case is to consider the purpose. When determining the purpose, think about the learning points you might wish to illustrate in the case, the people who will read the case study, and how it will eventually be presented to them. The best case study will be one that will allow you to demonstrate the usefulness of the concept being passed across. Further, even with the right content, the tone, language, and style of the case study determine how well it is taken in. When writing or presenting the case study, approach it from a straightforward expression style, one that will emphasize on the most part of the case with respect to the idea being put forth.

In the modern dynamic and current world, the most useful information will be the one which is up to date. For instance, in a business environment where one needs to forecast the demand for products they are developing, they will require up to date information on consumer preferences to help them make the right decisions. As a student writing a case study, one of the most important factors to consider when choosing a case for the study is its currency. This does not mean, however, that old case studies are useless. The key is to know when the current case is appropriate, and when it might be useful to incorporate concepts from an old case.

Most of the time, case study research involves one specific concept. Therefore, the complexity of the case chosen will determine how and whether the idea is passed across effectively. In situations where an appropriate case might be too complex, then, one might benefit from subdividing the case study into several different sections and choosing the most appropriate one.

As a student, the case study is one of the more effective research techniques. We hope that with these tips, choosing and writing case studies will definitely be much easier.

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When and How to Use a Case Study for Research

May 17, 2021 (Updated: May 4, 2023)

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What Is Case Study Research?

Types of case studies, when should you use a case study, case study benefits, case study limitations, how to write a case study.

Imagine your company receives a string of negative reviews online. You notice a few common themes among the complaints, but you still aren’t quite sure what went wrong. Or suppose an old blog post suddenly went viral, and you’d like to know why and how to do it again. In both of these situations, a case study could be the best way to find answers.

A case study is a process whereby researchers examine a specific subject in a thorough, detailed way. The subject of a case study could be an individual, a group, a community, a business, an organization, an event, or a phenomenon. Regardless of the type of subject, case studies are in-depth investigations designed to identify patterns and cause-and-effect relationships. Case studies are often used by researchers in the field of psychology , medicine, business, social work, anthropology, education, or political science.

Because they are singular in their focus and often rely on qualitative data, case studies tend to be highly subjective. The results of a single case study cannot always be generalized and applied to the larger population. However, case studies can be valuable tools for developing a thesis or illustrating a principle. They can help researchers understand, describe, compare, and evaluate different aspects of an issue or question.

Image via Flickr by plings

Case studies can be classified according to their purpose or their subject. For instance, a case study can focus on any of the following:

• A person:  Some case studies focus on one particular person. Often, the subject will be an individual with some rare characteristic or experience.
• A group:  Group case studies could look at a family, a group of coworkers, or a friend group. It could be people thrown together by circumstance or who share some bond or relationship. A group case study could even focus on an entire community of people.
• An organization:  An organizational case study could focus on a business, a nonprofit, an institution, or any other formal entity. The study could look at the people in the organization, the processes they use, or an incident at the organization.
• A location:  An event case study focuses on a specific area. It could be used to study environmental and population changes or to examine how people use the location.
• An event:  Event case studies can be used to cover anything from a natural disaster to a political scandal. Often, these case studies are conducted retrospectively, as an investigation into a past event.

In addition to different types of subjects, case studies often have different designs or purposes. Here are a few of the most common types of case studies:

• Explanatory:  An explanatory case study tries to explain the why or how behind something. This type of case study works well when studying an event or phenomenon, like an airplane crash or unexpected power outage.
• Descriptive:  A descriptive, or illustrative, case study is designed to shed light on an unfamiliar subject. Case studies like this provide in-depth, real-world examples of whatever the researcher wants to help the audience understand. For instance, a descriptive case study could focus on the experience of a mother with postpartum depression or on a young adult who has aged out of the foster care system.
• Exploratory:  An exploratory case study, or pilot case study, often serves as the first step in a larger research project. Researchers may use a case study to help them narrow their focus, draft a specific research question, and guide the parameters of a formal, large-scale study.
• Intrinsic:  An intrinsic case study has no goal beyond a deeper understanding of its subject. In this type of study, researchers are not trying to make generalized conclusions, challenge existing assumptions, or make any compare-and-contrast connections. The most interesting thing about the study is the subject itself.
• Critical Instance:  A critical instance case study is similar to an explanatory or intrinsic study. Like an intrinsic study, it may have no predetermined purpose beyond investigating the subject. Like an explanatory study, it may be used to explain a cause-and-effect relationship. A critical instance case study may also be used to call into question a commonly held assumption or popular theory.
• Instrumental:  An instrumental case study is the opposite of an intrinsic study because it serves a purpose beyond understanding the immediate subject. In this type of study, researchers explore a larger question through an individual case or cases. For instance, researchers could use a handful of case studies to investigate the relationship between social media use and happiness.
• Cumulative:  A cumulative, or collective, case study uses information from several past studies as the basis for a new study. Because it takes into account multiple case studies, a cumulative study allows for greater generalization than a single case study. It can also be a more time- and cost-effective option since it makes use of existing research.

Case studies are often used in the exploratory phase of research to gather qualitative data. They can also be used to create, support, or refute a hypothesis and guide future research. For instance, a marketing professional might conduct a case study to discover why a viral ad campaign was so successful . They can then take any lessons they glean from the case study and apply them to future marketing efforts. A psychologist could use a case study to form a theory about the best way to treat a specific disorder. That theory could then be tested later through a large-scale controlled study.

Case studies are a good way to explore a real-world topic in-depth, illustrate a point, discuss the implications or meaning of an event, or compare the experiences of different individuals. A trainer may use a case study to bring to life what would otherwise be an abstract series of recommended action steps or to spark a conversation about how to respond in a specific scenario. Similarly, professors can use case studies to highlight key concepts from a lecture and pose questions to test students’ understanding of the material.

In some situations, case studies are the only way to compile quantitative data in an ethical manner. For instance, many of the recommendations that doctors make regarding what is or is not safe during pregnancy are based on case studies. It wouldn’t be ethical to conduct a controlled study that exposes pregnant women to potentially harmful substances, so doctors rely on the anecdotal evidence provided by case studies to find correlations and draw their conclusions.

Case studies can also be used to gather data that would be otherwise impossible or impractical to obtain. Students often use case studies for their thesis or dissertation when they lack the time or resources to conduct large-scale research. Zoologists might use existing case studies to determine the success rate of reintroducing rehabilitated animals into the wild. A historian could use case studies to explore the strategies used by dictators to gain and maintain power.

Image via Flickr by calebmmartin

Case studies can be used on their own or as a complement to other research methods, depending on the situation. The examples above are just a few instances where case studies can be useful. Case studies also work well for the following:

Providing Insight Through Qualitative Data

Case studies generally provide more qualitative data as opposed to quantitative data , and that makes them an invaluable tool for gathering insight into complex topics. Psychologists, for instance, use case studies to better understand human behavior. Crafting theories on the motives behind human actions would be difficult with quantitative data alone. The information gleaned through case studies may be subjective, but so is much of what makes us human. As individuals, we each have a unique blend of emotions, attitudes, opinions, motivations, and behaviors. Objective quantitative data is rarely the best way to identify and explain these nuances.

By their very nature, case studies allow more more intensive, in-depth study than other research methods. Rather than aiming for a large sample size, case studies follow a single subject. Often case studies are conducted over a longer period of time, and the narrow focus allows researchers to gather more detail than would be possible in a study of thousands of people. The information gleaned may not be representative of the broader population, but it does provide richer insight into the subject than other research methods.

Identifying Avenues for Future Research

Case studies are often used as the first step in a larger research project. The results of a case study cannot necessarily be generalized, but they can help researchers narrow their focus. For instance, researchers in the medical field might conduct a case study on a patient who survived an injury that typically proves fatal.

Over the course of the study, researchers may identify two or three ways in which this patient’s situation differed from others they have seen. Perhaps they identify something unique in the patient’s DNA or lifestyle choices or in the steps doctors took to treat the injury. Letting that information guide them, researchers could use other methods to deepen their understanding of those factors and perhaps develop new treatments or preventative recommendations.

Case studies can also be used in the fields of social work, politics, and anthropology to draw attention to a widespread problem and spur more research. A detailed narrative about one person’s experience will inspire more compassion than an academic paper filled with quantitative data. Stories often have a greater impact than statistics.

Challenging, Testing, or Developing Theories

Case studies can be particularly useful in the process of forming and testing theories. A case study may lead researchers to form a new theory or call into a question an existing one. They are an invaluable tool for identifying exceptions to a rule or disproving conventional wisdom.

For instance, a medical professional may write a case study about a patient who exhibited atypical symptoms to assert that the list of symptoms for a condition should be expanded. A psychologist could use a case study to determine whether the new treatment they devised for depression is effective, or to demonstrate that existing treatment methods are flawed. As the result of a case study, a marketing professional could suggest that consumers values have changed and that marketing best practices should be updated accordingly.

Enabling the Study of Unique Subjects

Some subjects would be impossible, impractical, or unethical to study through other research methods. This is true in the case of extremely rare phenomenon, many aspects of human behavior, and even some medical conditions.

Suppose a medical professional would like to gather more information about multiple-birth pregnancies with four or more fetuses. More information would be helpful because we have less information about them, but the reason we have less information is because they are so rare. Conducting case studies of a few women who are currently pregnant with multiples or have given birth to multiples in the past may be the only practical way to research them.

Case studies can also be used to gain insight into historical events and natural phenomenon — things we are not able to repeat at will. Case studies have also been used to study subjects such as a feral child , child prodigies, rare psychological conditions, crisis response, and more.

Helping People Better Understand Nuanced Concepts

Educators incorporate case studies into their lectures for a reason. Walking students through a detailed case study can make the abstract seem more real and draw out the nuances of a concept. Case studies can facilitate engaging discussions, spark thoughtful questions, and give students a chance to apply what they have learned to real-world situations.

Outside the classroom, case studies can be used to illustrate complex ideas. For instance, a well-constructed case study can highlight the unintended consequences of a new piece of legislation or demonstrate that depression does not always manifest in an obvious way. Case studies can help readers and listeners understand and care about an issue that does not directly affect them.

Despite their benefits, case studies do come with a few limitations. Compared to other research methods, case studies are often at a disadvantage in terms of the following:

Replicability

In most cases, scientists strive to create experiments that can be repeated by others. That way, other scientists can perform their own research and compare their results to those of the initial study. Assuming these other scientists achieve similar results, the replicability of the experiment lends credibility to the findings and theories of the original researchers.

One limitation of case studies is that they are often difficult, if not impossible, to replicate. Although this fact does not diminish the value of case studies, it does demonstrate that case studies are not a good fit for every research problem — at least, not on their own. Additional research would have to be performed to corroborate the results and prove or disprove any generalized theories generated by a case study.

Generalization

Generalization is another area in which case studies cannot match other research methods. A case study can help us challenge existing theories and form new ones, but its results cannot necessarily be generalized. The data we gather from a case study is only valid for that specific subject, and we cannot assume that our conclusions apply to the broader population.

Researchers or readers can attempt to apply the principles from a particular case to similar situations or incorporate the results into a more comprehensive theory. However, a case study by itself can only prove the existence of certain possibilities and exceptions, not a general rule.

Reliability

The reliability of case studies may be called into question for two reasons. The first objection centers on the fallibility of human memory and the question of whether subjects are being honest. Many case studies rely on subjects to self-report biographical details, their state of mind, their thoughts and feelings, or their behaviors.

The second issue is the Hawthorne effect, which refers to the tendency of individuals to modify their behavior when they know they are being observed. This effect makes it nearly impossible for researchers to ensure that the observations and conclusions of their case study are reliable.

Researcher Bias

Researcher bias is another potential issue with case studies. The results of a case study are by nature subjective and qualitative rather than objective and qualitative, and any findings rely heavily on the observations and narrative provided by the researcher. Even the best researchers are still human, and no matter how hard they try to remain objective, they will not be able to keep their findings completely free of bias.

Researchers may have biases they are not even aware of. A researcher may over-identify with the subject and lose the benefit of a dispassionate outside perspective. If the researcher already has an opinion on the subject, they may subconsciously overlook or discount facts that contradict their pre-existing assumptions. Researcher bias can affect what the researcher observes and records, as well as how they interpret and apply their observations.

Case studies can be time-consuming and expensive to conduct. Crafting a thorough case study can be a lengthy project due to the intensive, detailed nature of this type of research. Plus, once the information has been gathered, it must be interpreted. Between the observation and analysis, a case study could take months or even years to complete. Researchers will need to be heavily involved in every step of the process, putting in a lot of time, energy, focus, and effort to ensure that the case study is as informative as possible.

Now that you understand the benefits, limitations, and types of case studies, you can follow these steps to write your own:

• Determine your objective.  Write out your research problem, question, or goal. If you aren’t sure, ask yourself questions like, “What am I trying to accomplish? What do I need to know? What will success look like?” Be clear and specific. Your answers will help you choose the right type of case study for your needs.
• Review the research.  Before delving into your case study, take some time to review the research that is already available. The information you gather during this preliminary research can help guide your efforts.
• Choose a subject.  Decide what or who the subject of your case study will be. For instance, if you are conducting a case study to find out how businesses have been affected by new CDC guidelines, you will need to choose a specific restaurant or retailer. In some cases, you may need to draft a release form for the subject to sign so that you will be able to publish your study.
• Gather information.  Case studies about a person, organization, or group may rely on questionnaires or interviews to gather information. If you are studying an event, you might use a combination of academic research and witness interviews. In some cases, you will record your own observations as part of the study.
• Write a report.  Most case studies culminate in a written report, similar to a research paper. Most case studies include five sections : an introduction, a literature review, an explanation of your methods, a discussion of your findings and the implications, followed by a conclusion.
• Publish your findings.  Once you’ve written your case study, consider the most engaging way to present your findings. A well-written research article is a good place to start, but going a step further will maximize the impact of your research. For instance, you could design an infographic to highlight key findings or commission an animated video to turn your case study into a visual narrative.

Whether research is your primary occupation or only an incidental part of your job, you can benefit from a solid understanding of what case studies are, how they work, and when to use them. Use the information and steps above to design and write a case study that will provide the answers you’re looking for.

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Types of Case Studies: a Comprehensive Guide

The art of writing involves multiple types of materials, which makes both writing and reading a pleasure that is informative and conducive to conveying knowledge and experience. However, when it comes to academic writing, there is a need to distinguish between the many types of materials based on their appropriateness to a certain kind of information.

Each type of material with its own structure and manner of writing is best suited for certain types of topics. The latter can range from anything like scientific articles or presentations of projects to various informative materials that tell the reader about new releases of products or services. In the given material, we shall identify the examples of a case study that can be used to great extent in a variety of topics. We shall also include examples of case study research to better illustrate how such a material can benefit the writer to effectively convey information, and the reader to better understand the key takeaways and evaluate value.

What is a case study?

The most common question that arises among new entrants into the art of writing when confronted with the need to write a case study is “ what are case studies ”? There is a wide variety of case studies meaning definitions available in the Internet, but all of them converge on a single one that essentially defines case studies as an in-depth analysis of a certain case, occurrence, story, or subject, with a detailed explanation of the impact thereof in the context of the real world.

Just as in hard science there are case study methods , so too in writing, there are numerous ways in which a case study can be written, elaborated, and explored to better divulge its value and meaning to the reader. The general concept of a case study is to better explain how that particular case arose, evolved, developed, and concluded, with well-defined and analyzed expressions of the consequences for the parties involved and even a statistical analysis.

The most common areas in which case studies are employed as a means of convening informational value are storytelling in business and scientific settings. As both areas are dominated by various cases of precedents and success or failure, the need to examine each in detail and allow readers to draw conclusions makes case studies an ideal go-to type of material for such purposes.

Types of subjects in a case study

The subjects that case studies can encompass are as numerous as there are topics to explore. Most commonly, case studies are used in science, legislative matters, technology-related areas, business, and other domains.

Most common subjects explored in case studies include success stories, certain technological, medical, or scientific breakthroughs, historical studies, and even academic writing.

What are the case study benefits?

The case study can provide a number of considerable advantages to writers as a type of material, which can help clearly define a relationship between the subject involved in the case and its context. The flexibility with which the data included in the material can be presented helps achieve both clarity and the necessary effect on the reader. Most importantly, case studies provide a clear outline of events in extensive detail and help establish a cause-effect relationship.

Essential types of case studies  

There are many types of case studies available to writers that they choose from in their strive to elaborate a certain instance and tailor the material for better digestibility by readers. The following is a short overview of the main types of case studies and their purposes:

Illustrative

As the name implies, an illustrative case study serves the purpose of illustrating a certain case. This type of material is used to describe a certain event in greater detail and delves into the circumstances of the underlying situation, with an emphasis on the causes and effects thereof.

Illustrative case studies are ideal for describing medical cases and patient illness histories, especially in the case of rare diseases. By exploring the course of the case and the circumstances that took place during it, the reader will have a clear picture of the situation and will be able to relate it to their own real-world standing to either replicate the case or avoid any pitfalls that might have been described in the material.

Such materials are also used to great effect in the business world, whereby companies describe their success stories and thus highlight their competencies to attract new clients and inspire awe in customers.

Exploratory

An exploratory case study is a very specific type of material that has the aim of encompassing a set of data as an initial research attempt for the purpose of identifying possible patterns. Such patterns, if any exist, can help create a model based on the data and then extrapolate it for further analysis, application, or research.

Exploratory case studies are primarily used in the scientific community by researchers who conduct experiments, or data analysts tasked with sifting through large arrays of data. The point of an exploratory case study is basically to make sense of a large buildup of information.

Cumulative case studies, as the name might suggest, accumulate information from various sources and compile it in a single material for the purpose of analysis, comparison, or evaluation. The data can be collected from different time periods, sources, or even places and combined for analysis in the search for patterns or other purposes.

As a rule, every cumulative case study must start with a founding question that will act as a hypothesis for further exploration of the compiled data.

Critical instance

As in critical thinking, which seeks to find relations between cause and effect, a critical instance case study is used to answer questions pertaining to the causes and consequences of a certain case. Such materials are ideal for making certain points about established concepts, placing emphasis on certain topics of interest within a larger framework of questions as outliers, and diving into the reasons for certain events.

Critical instance case studies are used extensively in criminalistics, legal proceedings, and experimental science, where a fresh look on certain questions is sometimes in order to identify a potential cause, or cause-effect relationship.

Intrinsic case studies perfectly leverage the term to explain the value of a certain question, rather than its relevance. Such materials are ideal for delving into the finer details of a case, explaining its uniqueness, qualities, characteristics, and other aspects.

Such materials are perfectly suited for focusing on certain topics, or highlighting previously unnoticed qualities about them.

Descriptive

As the term implies, descriptive case studies simply describe a certain case without any additional emphasis on questions, qualities, or effects. Such materials offer an objective look on a case and provide a hard, fact-based overview of the event.

A descriptive case study is commonly used in government-related materials, scientific research, medical records, and other areas, where there is no need to appeal to a reader, or to “sell” them a certain concept, company, or product.

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A step-by-step guide to causal study design using real-world data

• Open access
• Published: 19 June 2024

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• Sarah Ruth Hoffman 1 ,
• Nilesh Gangan 1 ,
• Xiaoxue Chen 2 ,
• Joseph L. Smith 1 ,
• Arlene Tave 1 ,
• Yiling Yang 1 ,
• Christopher L. Crowe 1 ,
• Susan dosReis 3 &
• Michael Grabner 1  

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Due to the need for generalizable and rapidly delivered evidence to inform healthcare decision-making, real-world data have grown increasingly important to answer causal questions. However, causal inference using observational data poses numerous challenges, and relevant methodological literature is vast. We endeavored to identify underlying unifying themes of causal inference using real-world healthcare data and connect them into a single schema to aid in observational study design, and to demonstrate this schema using a previously published research example. A multidisciplinary team (epidemiology, biostatistics, health economics) reviewed the literature related to causal inference and observational data to identify key concepts. A visual guide to causal study design was developed to concisely and clearly illustrate how the concepts are conceptually related to one another. A case study was selected to demonstrate an application of the guide. An eight-step guide to causal study design was created, integrating essential concepts from the literature, anchored into conceptual groupings according to natural steps in the study design process. The steps include defining the causal research question and the estimand; creating a directed acyclic graph; identifying biases and design and analytic techniques to mitigate their effect, and techniques to examine the robustness of findings. The cardiovascular case study demonstrates the applicability of the steps to developing a research plan. This paper used an existing study to demonstrate the relevance of the guide. We encourage researchers to incorporate this guide at the study design stage in order to elevate the quality of future real-world evidence.

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1 Introduction

Approximately 50 new drugs are approved each year in the United States (Mullard 2022 ). For all new drugs, randomized controlled trials (RCTs) are the gold-standard by which potential effectiveness (“efficacy”) and safety are established. However, RCTs cannot guarantee how a drug will perform in a less controlled context. For this reason, regulators frequently require observational, post-approval studies using “real-world” data, sometimes even as a condition of drug approval. The “real-world” data requested by regulators is often derived from insurance claims databases and/or healthcare records. Importantly, these data are recorded during routine clinical care without concern for potential use in research. Yet, in recent years, there has been increasing use of such data for causal inference and regulatory decision making, presenting a variety of methodologic challenges for researchers and stakeholders to consider (Arlett et al. 2022 ; Berger et al. 2017 ; Concato and ElZarrad 2022 ; Cox et al. 2009 ; European Medicines Agency 2023 ; Franklin and Schneeweiss 2017 ; Girman et al. 2014 ; Hernán and Robins 2016 ; International Society for Pharmacoeconomics and Outcomes Research (ISPOR) 2022 ; International Society for Pharmacoepidemiology (ISPE) 2020 ; Stuart et al. 2013 ; U.S. Food and Drug Administration 2018 ; Velentgas et al. 2013 ).

Current guidance for causal inference using observational healthcare data articulates the need for careful study design (Berger et al. 2017 ; Cox et al. 2009 ; European Medicines Agency 2023 ; Girman et al. 2014 ; Hernán and Robins 2016 ; Stuart et al. 2013 ; Velentgas et al. 2013 ). In 2009, Cox et al. described common sources of bias in observational data and recommended specific strategies to mitigate these biases (Cox et al. 2009 ). In 2013, Stuart et al. emphasized counterfactual theory and trial emulation, offered several approaches to address unmeasured confounding, and provided guidance on the use of propensity scores to balance confounding covariates (Stuart et al. 2013 ). In 2013, the Agency for Healthcare Research and Quality (AHRQ) released an extensive, 200-page guide to developing a protocol for comparative effectiveness research using observational data (Velentgas et al. 2013 ). The guide emphasized development of the research question, with additional chapters on study design, comparator selection, sensitivity analyses, and directed acyclic graphs (Velentgas et al. 2013 ). In 2014, Girman et al. provided a clear set of steps for assessing study feasibility including examination of the appropriateness of the data for the research question (i.e., ‘fit-for-purpose’), empirical equipoise, and interpretability, stating that comparative effectiveness research using observational data “should be designed with the goal of drawing a causal inference” (Girman et al. 2014 ). In 2017 , Berger et al. described aspects of “study hygiene,” focusing on procedural practices to enhance confidence in, and credibility of, real-world data studies (Berger et al. 2017 ). Currently, the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) maintains a guide on methodological standards in pharmacoepidemiology which discusses causal inference using observational data and includes an overview of study designs, a chapter on methods to address bias and confounding, and guidance on writing statistical analysis plans (European Medicines Agency 2023 ). In addition to these resources, the “target trial framework” provides a structured approach to planning studies for causal inferences from observational databases (Hernán and Robins 2016 ; Wang et al. 2023b ). This framework, published in 2016, encourages researchers to first imagine a clinical trial for the study question of interest and then to subsequently design the observational study to reflect the hypothetical trial (Hernán and Robins 2016 ).

While the literature addresses critical issues collectively, there remains a need for a framework that puts key components, including the target trial approach, into a simple, overarching schema (Loveless 2022 ) so they can be more easily remembered, and communicated to all stakeholders including (new) researchers, peer-reviewers, and other users of the research findings (e.g., practicing providers, professional clinical societies, regulators). For this reason, we created a step-by-step guide for causal inference using administrative health data, which aims to integrate these various best practices at a high level and complements existing, more specific guidance, including those from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the International Society for Pharmacoepidemiology (ISPE) (Berger et al. 2017 ; Cox et al. 2009 ; Girman et al. 2014 ). We demonstrate the application of this schema using a previously published paper in cardiovascular research.

This work involved a formative phase and an implementation phase to evaluate the utility of the causal guide. In the formative phase, a multidisciplinary team with research expertise in epidemiology, biostatistics, and health economics reviewed selected literature (peer-reviewed publications, including those mentioned in the introduction, as well as graduate-level textbooks) related to causal inference and observational healthcare data from the pharmacoepidemiologic and pharmacoeconomic perspectives. The potential outcomes framework served as the foundation for our conception of causal inference (Rubin 2005 ). Information was grouped into the following four concepts: (1) Defining the Research Question; (2) Defining the Estimand; (3) Identifying and Mitigating Biases; (4) Sensitivity Analysis. A step-by-step guide to causal study design was developed to distill the essential elements of each concept, organizing them into a single schema so that the concepts are clearly related to one another. References for each step of the schema are included in the Supplemental Table.

In the implementation phase we tested the application of the causal guide to previously published work (Dondo et al. 2017 ). The previously published work utilized data from the Myocardial Ischaemia National Audit Project (MINAP), the United Kingdom’s national heart attack register. The goal of the study was to assess the effect of β-blockers on all-cause mortality among patients hospitalized for acute myocardial infarction without heart failure or left ventricular systolic dysfunction. We selected this paper for the case study because of its clear descriptions of the research goal and methods, and the explicit and methodical consideration of potential biases and use of sensitivity analyses to examine the robustness of the main findings.

3.1 Overview of the eight steps

The step-by-step guide to causal inference comprises eight distinct steps (Fig.  1 ) across the four concepts. As scientific inquiry and study design are iterative processes, the various steps may be completed in a different order than shown, and steps may be revisited.

A step-by-step guide for causal study design

Abbreviations: GEE: generalized estimating equations; IPC/TW: inverse probability of censoring/treatment weighting; ITR: individual treatment response; MSM: marginal structural model; TE: treatment effect

Please refer to the Supplemental Table for references providing more in-depth information.

1 Ensure that the exposure and outcome are well-defined based on literature and expert opinion.

2 More specifically, measures of association are not affected by issues such as confounding and selection bias because they do not intend to isolate and quantify a single causal pathway. However, information bias (e.g., variable misclassification) can negatively affect association estimates, and association estimates remain subject to random variability (and are hence reported with confidence intervals).

3 This list is not exhaustive; it focuses on frequently encountered biases.

4 To assess bias in a nonrandomized study following the target trial framework, use of the ROBINS-I tool is recommended ( https://www.bmj.com/content/355/bmj.i4919 ).

5 Only a selection of the most popular approaches is presented here. Other methods exist; e.g., g-computation and g-estimation for both time-invariant and time-varying analysis; instrumental variables; and doubly-robust estimation methods. There are also program evaluation methods (e.g., difference-in-differences, regression discontinuities) that can be applied to pharmacoepidemiologic questions. Conventional outcome regression analysis is not recommended for causal estimation due to issues determining covariate balance, correct model specification, and interpretability of effect estimates.

6 Online tools include, among others, an E-value calculator for unmeasured confounding ( https://www.evalue-calculator.com /) and the P95 outcome misclassification estimator ( http://apps.p-95.com/ISPE /).

3.2 Defining the Research question (step 1)

The process of designing a study begins with defining the research question. Research questions typically center on whether a causal relationship exists between an exposure and an outcome. This contrasts with associative questions, which, by their nature, do not require causal study design elements because they do not attempt to isolate a causal pathway from a single exposure to an outcome under study. It is important to note that the phrasing of the question itself should clarify whether an association or a causal relationship is of interest. The study question “Does statin use reduce the risk of future cardiovascular events?” is explicitly causal and requires that the study design addresses biases such as confounding. In contrast, the study question “Is statin use associated with a reduced risk of future cardiovascular events?” can be answered without control of confounding since the word “association” implies correlation. Too often, however, researchers use the word “association” to describe their findings when their methods were created to address explicitly causal questions (Hernán 2018 ). For example, a study that uses propensity score-based methods to balance risk factors between treatment groups is explicitly attempting to isolate a causal pathway by removing confounding factors. This is different from a study that intends only to measure an association. In fact, some journals may require that the word “association” be used when causal language would be more appropriate; however, this is beginning to change (Flanagin et al. 2024 ).

3.3 Defining the estimand (steps 2, 3, 4)

The estimand is the causal effect of research interest and is described in terms of required design elements: the target population for the counterfactual contrast, the kind of effect, and the effect/outcome measure.

In Step 2, the study team determines the target population of interest, which depends on the research question of interest. For example, we may want to estimate the effect of the treatment in the entire study population, i.e., the hypothetical contrast between all study patients taking the drug of interest versus all study patients taking the comparator (the average treatment effect; ATE). Other effects can be examined, including the average treatment effect in the treated or untreated (ATT or ATU).When covariate distributions are the same across the treated and untreated populations and there is no effect modification by covariates, these effects are generally the same (Wang et al. 2017 ). In RCTs, this occurs naturally due to randomization, but in non-randomized data, careful study design and statistical methods must be used to mitigate confounding bias.

In Step 3, the study team decides whether to measure the intention-to-treat (ITT), per-protocol, or as-treated effect. The ITT approach is also known as “first-treatment-carried-forward” in the observational literature (Lund et al. 2015 ). In trials, the ITT measures the effect of treatment assignment rather than the treatment itself, and in observational data the ITT can be conceptualized as measuring the effect of treatment as started . To compute the ITT effect from observational data, patients are placed into the exposure group corresponding to the treatment that they initiate, and treatment switching or discontinuation are purposely ignored in the analysis. Alternatively, a per-protocol effect can be measured from observational data by classifying patients according to the treatment that they initiated but censoring them when they stop, switch, or otherwise change treatment (Danaei et al. 2013 ; Yang et al. 2014 ). Finally, “as-treated” effects are estimated from observational data by classifying patients according to their actual treatment exposure during follow-up, for example by using multiple time windows to measure exposure changes (Danaei et al. 2013 ; Yang et al. 2014 ).

Step 4 is the final step in specifying the estimand in which the research team determines the effect measure of interest. Answering this question has two parts. First, the team must consider how the outcome of interest will be measured. Risks, rates, hazards, odds, and costs are common ways of measuring outcomes, but each measure may be best suited to a particular scenario. For example, risks assume patients across comparison groups have equal follow-up time, while rates allow for variable follow-up time (Rothman et al. 2008 ). Costs may be of interest in studies focused on economic outcomes, including as inputs to cost-effectiveness analyses. After deciding how the outcome will be measured, it is necessary to consider whether the resulting quantity will be compared across groups using a ratio or a difference. Ratios convey the effect of exposure in a way that is easy to understand, but they do not provide an estimate of how many patients will be affected. On the other hand, differences provide a clearer estimate of the potential public health impact of exposure; for example, by allowing the calculation of the number of patients that must be treated to cause or prevent one instance of the outcome of interest (Tripepi et al. 2007 ).

3.4 Identifying and mitigating biases (steps 5, 6, 7)

Observational, real-world studies can be subject to multiple potential sources of bias, which can be grouped into confounding, selection, measurement, and time-related biases (Prada-Ramallal et al. 2019 ).

In Step 5, as a practical first approach in developing strategies to address threats to causal inference, researchers should create a visual mapping of factors that may be related to the exposure, outcome, or both (also called a directed acyclic graph or DAG) (Pearl 1995 ). While creating a high-quality DAG can be challenging, guidance is increasingly available to facilitate the process (Ferguson et al. 2020 ; Gatto et al. 2022 ; Hernán and Robins 2020 ; Rodrigues et al. 2022 ; Sauer 2013 ). The types of inter-variable relationships depicted by DAGs include confounders, colliders, and mediators. Confounders are variables that affect both exposure and outcome, and it is necessary to control for them in order to isolate the causal pathway of interest. Colliders represent variables affected by two other variables, such as exposure and outcome (Griffith et al. 2020 ). Colliders should not be conditioned on since by doing so, the association between exposure and outcome will become distorted. Mediators are variables that are affected by the exposure and go on to affect the outcome. As such, mediators are on the causal pathway between exposure and outcome and should also not be conditioned on, otherwise a path between exposure and outcome will be closed and the total effect of the exposure on the outcome cannot be estimated. Mediation analysis is a separate type of analysis aiming to distinguish between direct and indirect (mediated) effects between exposure and outcome and may be applied in certain cases (Richiardi et al. 2013 ). Overall, the process of creating a DAG can create valuable insights about the nature of the hypothesized underlying data generating process and the biases that are likely to be encountered (Digitale et al. 2022 ). Finally, an extension to DAGs which incorporates counterfactual theory is available in the form of Single World Intervention Graphs (SWIGs) as described in a 2013 primer (Richardson and Robins 2013 ).

In Step 6, researchers comprehensively assess the possibility of different types of bias in their study, above and beyond what the creation of the DAG reveals. Many potential biases have been identified and summarized in the literature (Berger et al. 2017 ; Cox et al. 2009 ; European Medicines Agency 2023 ; Girman et al. 2014 ; Stuart et al. 2013 ; Velentgas et al. 2013 ). Every study can be subject to one or more biases, each of which can be addressed using one or more methods. The study team should thoroughly and explicitly identify all possible biases with consideration for the specifics of the available data and the nuances of the population and health care system(s) from which the data arise. Once the potential biases are identified and listed, the team can consider potential solutions using a variety of study design and analytic techniques.

In Step 7, the study team considers solutions to the biases identified in Step 6. “Target trial” thinking serves as the basis for many of these solutions by requiring researchers to consider how observational studies can be designed to ensure comparison groups are similar and produce valid inferences by emulating RCTs (Labrecque and Swanson 2017 ; Wang et al. 2023b ). Designing studies to include only new users of a drug and an active comparator group is one way of increasing the similarity of patients across both groups, particularly in terms of treatment history. Careful consideration must be paid to the specification of the time periods and their relationship to inclusion/exclusion criteria (Suissa and Dell’Aniello 2020 ). For instance, if a drug is used intermittently, a longer wash-out period is needed to ensure adequate capture of prior use in order to avoid bias (Riis et al. 2015 ). The study team should consider how to approach confounding adjustment, and whether both time-invariant and time-varying confounding may be present. Many potential biases exist, and many methods have been developed to address them in order to improve causal estimation from observational data. Many of these methods, such as propensity score estimation, can be enhanced by machine learning (Athey and Imbens 2019 ; Belthangady et al. 2021 ; Mai et al. 2022 ; Onasanya et al. 2024 ; Schuler and Rose 2017 ; Westreich et al. 2010 ). Machine learning has many potential applications in the causal inference discipline, and like other tools, must be used with careful planning and intentionality. To aid in the assessment of potential biases, especially time-related ones, and the development of a plan to address them, the study design should be visualized (Gatto et al. 2022 ; Schneeweiss et al. 2019 ). Additionally, we note the opportunity for collaboration across research disciplines (e.g., the application of difference-in-difference methods (Zhou et al. 2016 ) to the estimation of comparative drug effectiveness and safety).

3.5 Quality Control & sensitivity analyses (step 8)

Causal study design concludes with Step 8, which includes planning quality control and sensitivity analyses to improve the internal validity of the study. Quality control begins with reviewing study output for prima facie validity. Patient characteristics (e.g., distributions of age, sex, region) should align with expected values from the researchers’ intuition and the literature, and researchers should assess reasons for any discrepancies. Sensitivity analyses should be conducted to determine the robustness of study findings. Researchers can test the stability of study estimates using a different estimand or type of model than was used in the primary analysis. Sensitivity analysis estimates that are similar to those of the primary analysis might confirm that the primary analysis estimates are appropriate. The research team may be interested in how changes to study inclusion/exclusion criteria may affect study findings or wish to address uncertainties related to measuring the exposure or outcome in the administrative data by modifying the algorithms used to identify exposure or outcome (e.g., requiring hospitalization with a diagnosis code in a principal position rather than counting any claim with the diagnosis code in any position). As feasible, existing validation studies for the exposure and outcome should be referenced, or new validation efforts undertaken. The results of such validation studies can inform study estimates via quantitative bias analyses (Lanes and Beachler 2023 ). The study team may also consider biases arising from unmeasured confounding and plan quantitative bias analyses to explore how unmeasured confounding may impact estimates. Quantitative bias analysis can assess the directionality, magnitude, and uncertainty of errors arising from a variety of limitations (Brenner and Gefeller 1993 ; Lash et al. 2009 , 2014 ; Leahy et al. 2022 ).

3.6 Illustration using a previously published research study

In order to demonstrate how the guide can be used to plan a research study utilizing causal methods, we turn to a previously published study (Dondo et al. 2017 ) that assessed the causal relationship between the use of 𝛽-blockers and mortality after acute myocardial infarction in patients without heart failure or left ventricular systolic dysfunction. The investigators sought to answer a causal research question (Step 1), and so we proceed to Step 2. Use (or no use) of 𝛽-blockers was determined after discharge without taking into consideration discontinuation or future treatment changes (i.e., intention-to-treat). Considering treatment for whom (Step 3), both ATE and ATT were evaluated. Since survival was the primary outcome, an absolute difference in survival time was chosen as the effect measure (Step 4). While there was no explicit directed acyclic graph provided, the investigators specified a list of confounders.

Robust methodologies were established by consideration of possible sources of biases and addressing them using viable solutions (Steps 6 and 7). Table  1 offers a list of the identified potential biases and their corresponding solutions as implemented. For example, to minimize potential biases including prevalent-user bias and selection bias, the sample was restricted to patients with no previous use of 𝛽-blockers, no contraindication for 𝛽-blockers, and no prescription of loop diuretics. To improve balance across the comparator groups in terms of baseline confounders, i.e., those that could influence both exposure (𝛽-blocker use) and outcome (mortality), propensity score-based inverse probability of treatment weighting (IPTW) was employed. However, we noted that the baseline look-back period to assess measured covariates was not explicitly listed in the paper.

Quality control and sensitivity analysis (Step 8) is described extensively. The overlap of propensity score distributions between comparator groups was tested and confounder balance was assessed. Since observations in the tail-end of the propensity score distribution may violate the positivity assumption (Crump et al. 2009 ), a sensitivity analysis was conducted including only cases within 0.1 to 0.9 of the propensity score distribution. While not mentioned by the authors, the PS tails can be influenced by unmeasured confounders (Sturmer et al. 2021 ), and the findings were robust with and without trimming. An assessment of extreme IPTW weights, while not included, would further help increase confidence in the robustness of the analysis. An instrumental variable approach was employed to assess potential selection bias due to unmeasured confounding, using hospital rates of guideline-indicated prescribing as the instrument. Additionally, potential bias caused by missing data was attenuated through the use of multiple imputation, and separate models were built for complete cases only and imputed/complete cases.

4 Discussion

We have described a conceptual schema for designing observational real-world studies to estimate causal effects. The application of this schema to a previously published study illuminates the methodologic structure of the study, revealing how each structural element is related to a potential bias which it is meant to address. Real-world evidence is increasingly accepted by healthcare stakeholders, including the FDA (Concato and Corrigan-Curay 2022 ; Concato and ElZarrad 2022 ), and its use for comparative effectiveness and safety assessments requires appropriate causal study design; our guide is meant to facilitate this design process and complement existing, more specific, guidance.

Existing guidance for causal inference using observational data includes components that can be clearly mapped onto the schema that we have developed. For example, in 2009 Cox et al. described common sources of bias in observational data and recommended specific strategies to mitigate these biases, corresponding to steps 6–8 of our step-by-step guide (Cox et al. 2009 ). In 2013, the AHRQ emphasized development of the research question, corresponding to steps 1–4 of our guide, with additional chapters on study design, comparator selection, sensitivity analyses, and directed acyclic graphs which correspond to steps 7 and 5, respectively (Velentgas et al. 2013 ). Much of Girman et al.’s manuscript (Girman et al. 2014 ) corresponds with steps 1–4 of our guide, and the matter of equipoise and interpretability specifically correspond to steps 3 and 7–8. The current ENCePP guide on methodological standards in pharmacoepidemiology contains a section on formulating a meaningful research question, corresponding to step 1, and describes strategies to mitigate specific sources of bias, corresponding to steps 6–8 (European Medicines Agency 2023 ). Recent works by the FDA Sentinel Innovation Center (Desai et al. 2024 ) and the Joint Initiative for Causal Inference (Dang et al. 2023 ) provide more advanced exposition of many of the steps in our guide. The target trial framework contains guidance on developing seven components of the study protocol, including eligibility criteria, treatment strategies, assignment procedures, follow-up period, outcome, causal contrast of interest, and analysis plan (Hernán and Robins 2016 ). Our work places the target trial framework into a larger context illustrating its relationship with other important study planning considerations, including the creation of a directed acyclic graph and incorporation of prespecified sensitivity and quantitative bias analyses.

Ultimately, the feasibility of estimating causal effects relies on the capabilities of the available data. Real-world data sources are complex, and the investigator must carefully consider whether the data on hand are sufficient to answer the research question. For example, a study that relies solely on claims data for outcome ascertainment may suffer from outcome misclassification bias (Lanes and Beachler 2023 ). This bias can be addressed through medical record validation for a random subset of patients, followed by quantitative bias analysis (Lanes and Beachler 2023 ). If instead, the investigator wishes to apply a previously published, claims-based algorithm validated in a different database, they must carefully consider the transportability of that algorithm to their own study population. In this way, causal inference from real-world data requires the ability to think creatively and resourcefully about how various data sources and elements can be leveraged, with consideration for the strengths and limitations of each source. The heart of causal inference is in the pairing of humility and creativity: the humility to acknowledge what the data cannot do, and the creativity to address those limitations as best as one can at the time.

4.1 Limitations

As with any attempt to synthesize a broad array of information into a single, simplified schema, there are several limitations to our work. Space and useability constraints necessitated simplification of the complex source material and selections among many available methodologies, and information about the relative importance of each step is not currently included. Additionally, it is important to consider the context of our work. This step-by-step guide emphasizes analytic techniques (e.g., propensity scores) that are used most frequently within our own research environment and may not include less familiar study designs and analytic techniques. However, one strength of the guide is that additional designs and techniques or concepts can easily be incorporated into the existing schema. The benefit of a schema is that new information can be added and is more readily accessed due to its association with previously sorted information (Loveless 2022 ). It is also important to note that causal inference was approached as a broad overarching concept defined by the totality of the research, from start to finish, rather than focusing on a particular analytic technique, however we view this as a strength rather than a limitation.

Finally, the focus of this guide was on the methodologic aspects of study planning. As a result, we did not include steps for drafting or registering the study protocol in a public database or for communicating results. We strongly encourage researchers to register their study protocols and communicate their findings with transparency. A protocol template endorsed by ISPOR and ISPE for studies using real-world data to evaluate treatment effects is available (Wang et al. 2023a ). Additionally, the steps described above are intended to illustrate an order of thinking in the study planning process, and these steps are often iterative. The guide is not intended to reflect the order of study execution; specifically, quality control procedures and sensitivity analyses should also be formulated up-front at the protocol stage.

5 Conclusion

We outlined steps and described key conceptual issues of importance in designing real-world studies to answer causal questions, and created a visually appealing, user-friendly resource to help researchers clearly define and navigate these issues. We hope this guide serves to enhance the quality, and thus the impact, of real-world evidence.

Data availability

No datasets were generated or analysed during the current study.

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Hoffman, S.R., Gangan, N., Chen, X. et al. A step-by-step guide to causal study design using real-world data. Health Serv Outcomes Res Method (2024). https://doi.org/10.1007/s10742-024-00333-6

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Financial Case Study: AI Red Teaming

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In a rapidly evolving financial landscape, the integrity of AI-driven fraud detection systems is paramount. This case study explores how a leading global financial services company partnered with HiddenLayer to fortify its machine-learning models against potential adversarial threats. With over 50 million users and billions of transactions annually, the company faced the dual challenge of maintaining an optimal customer experience while combating sophisticated fraud. HiddenLayer’s AI Red Teaming initiative was crucial in identifying vulnerabilities and enhancing the security of their AI models, ensuring robust fraud detection without compromising user satisfaction.

Company Overview

A financial services company engaged the HiddenLayer Professional Services team to conduct a red team evaluation of machine learning models used to detect and intercept fraud during financial transactions. The primary purpose of the assessment was to identify weaknesses in their AI model classifications that adversaries could exploit to conduct fraudulent activities on the platform without triggering detection, resulting in millions of dollars of potential losses annually.  

Hitting the Target: Ensuring an Optimal Customer Experience and Lowering Losses Amidst Rising Fraud Risks

With over 50 million users and facilitating more than 5 billion transactions annually, our customer grappled with the ongoing challenge of minimizing customer experience issues while simultaneously combating fraud. The delicate balance required cutting-edge AI and ML models to detect and intercept fraudulent transactions effectively. With these models at the core of their transaction operations, their

commitment to stellar customer experiences and the need for advanced security for AI led them to engage with HiddenLayer for a comprehensive red teaming initiative.

Discovery And Selection Of Hiddenlayer

An existing customer referred the customer to HiddenLayer, who recognized our deep domain expertise in cyber and data science and our experience in automated adversarial attack tools. Additionally, HiddenLayer’s flexible pricing model aligned with the customer’s needs, making HiddenLayer the clear choice for their red teaming endeavor.

Key Selection Criteria

• Deep Expertise: Proficiency across cyber and data science modalities.
• Adversarial Attack Experience: Experience in detecting and mitigating automated adversarial attack tools.
• Flexible Pricing: The pricing model offered the flexibility sought for the client’s unique requirements.

Objectives of AI Red Teaming 

• Identify Vulnerable Features: Pinpoint features within the models are susceptible to an attacker’s influence, which could potentially substantially impact classification outcomes trending towards legitimacy.
• Create Adversarial Examples: Develop adversarial examples by modifying the fewest features in inputs classified as fraudulent, transitioning the classification from fraudulent to legitimate.
• Improve Model Classification: Identify areas for improvement within the target models to enhance the accuracy of classifying fraudulent activities.

Hiddenlayer’s Mitigation

Alongside existing security controls, the introduction of inference-time monitoring for model inputs and outputs to detect targeted attacks against the models allowed the ability to flag and block suspected adversarial abuse.

HiddenLayer’s AI Security (AISec) Platform, which includes AI Detection and Response for Gen AI (AIDR), provides real-time, scalable, and unobtrusive inference-time monitoring for all model types. AIDR can be used to audit all existing models for adversarial abuse and ongoing prevention of abuse. AIDR does not require access to the customer’s data or models, as all detections are performed using vectorized inputs and outputs.

AIDR provides protection against common adversarial techniques, including model extraction/theft, tampering, data poisoning/model injection, and inference.

Impact Of Hiddenlayer

The core purpose of HiddenLayer’s AI Red Teaming Assessment was to uncover weaknesses in model classification that adversaries could exploit for fraudulent activities without triggering detection. Now armed with a prioritized list of identified exploits, our client can channel their invaluable resources, involving data science and cyber teams, towards mitigation and remediation efforts with maximum impact. The result is an enhanced security posture for the entire platform without introducing additional friction for internal or external customers.

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The case study approach

Sarah crowe.

1 Division of Primary Care, The University of Nottingham, Nottingham, UK

Kathrin Cresswell

2 Centre for Population Health Sciences, The University of Edinburgh, Edinburgh, UK

Ann Robertson

3 School of Health in Social Science, The University of Edinburgh, Edinburgh, UK

Anthony Avery

Aziz sheikh.

The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables ​ Tables1, 1 , ​ ,2, 2 , ​ ,3 3 and ​ and4) 4 ) and those of others to illustrate our discussion[ 3 - 7 ].

Example of a case study investigating the reasons for differences in recruitment rates of minority ethnic people in asthma research[ 3 ]

Example of a case study investigating the process of planning and implementing a service in Primary Care Organisations[ 4 ]

Example of a case study investigating the introduction of the electronic health records[ 5 ]

Example of a case study investigating the formal and informal ways students learn about patient safety[ 6 ]

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table ​ (Table5), 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Definitions of a case study

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table ​ (Table1), 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables ​ Tables2, 2 , ​ ,3 3 and ​ and4) 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 - 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table ​ (Table2) 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables ​ Tables2 2 and ​ and3, 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table ​ (Table4 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table ​ (Table6). 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

Example of epistemological approaches that may be used in case study research

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table ​ Table7 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

Example of a checklist for rating a case study proposal[ 8 ]

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table ​ (Table3), 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table ​ (Table1) 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table ​ Table3) 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 - 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table ​ (Table2 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table ​ (Table1 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table ​ (Table3 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table ​ (Table4 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table ​ Table3, 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table ​ (Table4), 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table ​ Table8 8 )[ 8 , 18 - 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table ​ (Table9 9 )[ 8 ].

Potential pitfalls and mitigating actions when undertaking case study research

Stake's checklist for assessing the quality of a case study report[ 8 ]

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1471-2288/11/100/prepub

Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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How to Determine the Scope of Research | Examples & Tips

Introduction

What is the scope of a study, what is a research scope example, what is the purpose of the research scope, what considerations are relevant to the research scope, how do i write the scope in a report.

The scope of a research project is one of the more important yet sometimes understated aspects of a study. The scope of the study explains what the researchers are examining and what environment they are studying.

This article explains the general purpose of the research scope, how it informs the broader study at hand, and how it can be incorporated in a research paper to establish the necessary transparency and rigor for your research audience.

Scientific knowledge very rarely, if ever, produces universal axioms. The boiling point of water changes depending on the amount of pressure in the air and, by extension, the altitude you are at relative to sea level when you boil water. What looks like polite behavior in a given culture may look rude in another. The definition of beauty is bound to change as people get older.

Similarly, research findings that aren't contextualized are less persuasive. If you are reading a study that looks at interactional patterns between parents and their children, it's important to have a clear sense of the theoretical lens , data collection , and analysis in order to determine the extent to which the findings are applicable across contexts.

In a nutshell, the scope tells you what the researchers are looking at and are not looking at. It provides the context necessary to understand the research, how it was conducted, and what findings it generated.

Conversely, establishing the bounds of the scope also clarify what research inquiries are not addressed in the study, ensuring that the study's argumentation is clearly grounded in the theory, data, and analysis.

Let's imagine an example of a research study examining best practices for mental health. The research design centers on a survey study with a target population of college students with part-time jobs in addition to their coursework.

The researchers can focus on any number of things affecting mental health, including lifestyle factors such as sleep, socioeconomic factors such as income, and even influences further afield like the political alignment of friends and family.

Certainly, any of these things can have a profound impact on one's mental health. But when there are so many things to examine, it's necessary to narrow down what the research project at hand should examine.

The scope of the study can come down to any number of things, including the researchers' interest, the current state of theoretical development on the subject of mental health, and the design of the study, particularly how the data is collected. It might even boil down to influences like geographical location, which can determine the kind of research participants involved in the study.

All of these factors can inform an explicit description of the scope, which might look like this if found in the methodology section of a paper:

"In this study, the researchers focused on surveying college students over four months, roughly the same time frame as a semester at a university in the United States. Surveys were distributed to all college students, but this paper will narrow the data analysis to those students who reported having part-time jobs. This refined lens aligns with our interest in examining work-related factors contributing to negative mental health outcomes, as established in previous studies."

The above example of a study's scope highlights what the researchers focused on during the study and while analyzing the data. The researchers chose to study a narrow subset of their data to generate insights most applicable to their research interests. The researchers might also analyze the proportion of students that reported having part-time jobs to give a broader description of the study body, but they clearly focus on understanding the mental health of students with part-time jobs.

Moreover, the narrow scope allows the researchers to focus on a small number of elements in the relationship between mental health and work, which allows the researchers to make deeper contributions to this specific part of the conversation around students' mental health.

Defining the scope of the study benefits both the researcher and their audience. Ultimately, establishing transparency in a research project focuses the data collection and analysis processes and makes the findings more compelling and persuasive.

Describing the scope can clarify what specific concepts should be used and examined during the course of the study. A good scope can keep the researcher focused on what data to collect and what ancillary developments, however interesting or useful, should be discarded or left to another study. Setting a clear scope can greatly help researchers maintain a coherent fit between their research question, collected data, and ultimate findings. Journal editors and reviewers often reject papers for publication because of a lack of fit between these important elements, which highlights the value of a clear research scope for conducting rigorous research.

In logistical terms, a well-defined scope also ensures the feasibility of a study by limiting the researcher's lens to a small but manageable set of factors to observe and analyze during the course of the study. Conversely, an unfocused study makes the collection of data a significant challenge when the researcher is left to document as much as possible, potentially gathering all kinds of data that may not be relevant to a given research question , while not gathering enough of the appropriate data that can address a research inquiry.

The research audience also requires an understanding of the scope of the study to determine the relevance of the findings to their own research inquiry. Readers of research bring their own assumptions and preconceived notions about what to look at in a given context. A well-written scope, on the other hand, gives readers clear guidance on what to look for in the study's analysis and findings.

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Besides the research area being studied, the scope of a study has a clear description of most of the following aspects. Understanding what makes rigorous research and what readers of research look for in a well-crafted study will be useful for describing the scope of a research project.

Target population

The kind of research participants you are including in a study informs what theories are relevant and how the study should be designed. Are you researching children, young adults, or older professionals? Do they belong to a specific culture or community? Are they connected or related to each other in some way or do they just happen to belong to the same demographic?

Because qualitative, social science research seldom yields universal theories, it's important to narrow the scope of a study down to a specific set of the population. The more specific the scope, the more that the findings and resulting theoretical developments can be appropriately contextualized and thus inform how other researchers can build on those insights.

Geographical location

The geographical location covered by the study provides a necessary context for any study in the social sciences. Even if you narrow the targeted population to a specific demographic, what is true for that population in one country or region may not be true for another.

As a result, a scope that describes the location of the study explains where the findings are most relevant and where they might be relevant for further study.

Data collection

If you are conducting observational or ethnographic research , it may seem like you are facing a firehose when it comes to collecting data. Even interviews , focus groups , and surveys can provide a torrent of data, much of which may not be relevant to your inquiry if the study design isn't refined.

Without a sufficiently defined scope that identifies what aspects of the world you are looking at, the data you collect may become unmanageable at best. When crafting your study, develop the scope to determine the specific topics and aspects worth exploring.

In academic publishing , reviewers and editors need a clear understanding of the scope of the study in a manuscript when evaluating the research. Despite its importance, however, the scope doesn't necessarily have its own explicit section in a research paper.

That said, you can describe the study's scope in key areas of your research writing. Here are some of the important sections in a typical research paper for academic writing where a description of the scope is key.

Literature review

Any study disseminated for academic publishing requires a thorough understanding of the current research and existing theories that are relevant to your study. In turn, the literature review also defines the aspects of the phenomenon or concepts that you can study for the purpose of theoretical development.

Rely on the key theories in the literature review to define a useful scope that identifies key aspects of the theoretical framework that will inform the data collection and analysis .

Problem statement

A well-crafted problem statement generally sets the stage for what knowledge is missing and what novel and interesting insights can be uncovered in new research. As a result, a clear understanding of the research scope helps define the problem that a new research project seeks to address.

When incorporating a problem statement in your research paper, be sure to explicitly detail the rationale for problematizing the phenomenon you are researching.

Research question

Research questions define the relationships between the relevant concepts or phenomena being explored, and thus provide evidence of a scope that has been thoughtfully planned. Use the wording of your research question to highlight what is the central focus and, thus, the scope of the study.

At minimum, the scope of the study should narrow the focus of data collection and data analysis to the study of certain concepts relevant to addressing the given research question. Qualitative research methods can often result in open-ended data collection that can yield many insights, only a few of which may directly address the research inquiry.

Narrowing the collection of data to a set of relevant criteria can help the researcher avoid any unnecessary rabbit holes that might complicate the later analysis with irrelevant information.

Limitations

Research scope and limitations go hand in hand because, together, they define what is studied within a research project and what is not. Moreover, a good description of the study's scope can also provide direction, by way of the description of limitations, about what inquiries other researchers could pursue next.

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Exercise and Attention-Deficit/Hyperactivity Disorder (ADHD): Emerging Research from Dr. Meghan Edmondson

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According to the National Institute for Mental Health, nearly 11 percent of children in the United States aged 5-17 have been diagnosed with Attention-Deficit/Hyperactivity Disorder (ADHD), a common neurodevelopmental disorder that results in difficulties with focused attention, impulsiveness and hyperactivity. Meghan Edmondson, Ph.D., RN, CCRN , assistant professor for Kent State University College of Nursing, studies the disorder which often persists through adolescence and into adulthood. Specifically, her research centers on improving daily life for those with ADHD, including exercise as an intervention, self-management of the disorder, and recognition of ADHD as a barrier to self-management of other chronic conditions.

Before delving into her current research exploring ADHD and the possible benefits of exercise, Edmondson developed expertise in using electronic health record data for the secondary purpose of research. In addition to her more than 13 years of experience as a critical care nurse, Edmondson’s background includes expertise with exercise science and management of large data sets. She was the lead author on the publication, “Challenges Frequently Encountered in the Secondary Use of Electronic Medical Record Data for Research,” for the journal Computers, Informatics, Nursing: CIN . For her doctoral studies, Edmondson completed dissertation research evaluating mortality and length of stay after critical care transport. Using electronic health record data from transport records, she performed a secondary data analysis to describe differences in mortality and length of stay for patients transferred to Surgical Intensive Care as opposed to other types of Intensive Care Units. She also identified factors predictive of higher mortality and longer length of stay. An article associated with her study findings, “Outcomes After Interhospital Critical Care Transfer,” is currently in press for Air Medical Journal.

Edmondson’s own personal journey with ADHD, background in exercise science and passion for helping others led to a shift in her research focus to better understand and improve ADHD symptoms, especially for emerging adults since difficulties stemming from ADHD have not been widely studied for this population. Her primary research goal is to generate clinically useful knowledge for people with ADHD and their healthcare providers, including the potential benefits of an exercise prescription. Her current study, “Effect of Low, Moderate, and High Intensity Exercise on Executive Function, Functional Impairment, and Symptom Severity in ADHD,” is designed to answer intensity-related research questions. Ultimately, Edmondson seeks to provide the right self-management tools to people with ADHD which allows them to both harness their strengths and minimize the impact of their weaknesses. In 2024, Edmondson received the competitive Kent State University Brain Health Research Institute (BHRI) Pilot Grant Program Gold Award to conduct this study with assistance by her mentorship team and lay the groundwork for future federal funding for this important area of research.

Edmondson earned her Ph.D. in Nursing from Case Western Reserve University and a Bachelor of Science in Nursing from the University of Texas Health Science Center. She also completed a Bachelor of Science in Exercise Science from the University of Houston. Edmondson currently serves as Fellow for the Healthy Communities Research Institute Grant Academy at Kent State University. Her affiliations include Children and Adults with ADHD (CHADD), as well as the Kent State University Brain Health Research Institute, Healthy Communities Research Institute and Neurodiversity Research Initiative.

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Preventing suicide among youth in juvenile detention

By Implementation Research Institute (IRI) | July 1, 2024

Translational Science Benefits

Suicide is the second leading cause of death among 10- to 25-year-olds. 1 Suicidal behaviors are three to four times more likely among youths in the juvenile legal system 2–5 than among their peers outside the system. Fifty percent of youths in juvenile detention experience suicidal ideation. 6 Because of social and structural factors, including racism, 7 Black youths are disproportionately represented in detention. 8 Thus, placement in detention is an event that puts youths at high risk for suicidal behavior and that disproportionately affects Black youths. The Zero Suicide model is the national strategy for suicide prevention in healthcare. 9,10 The core clinical elements include IDENTIFY (i.e., evidence-based suicide screening and risk assessment), ENGAGE (i.e., appropriate pathways of care), TREAT (i.e., evidence-based suicide intervention), and TRANSITION (i.e., care coordination). We seek to develop a Zero Suicide model for juvenile detention to support youth at-risk for suicide and to ensure that existing disparities in mental health care for Black youths are not worsened. 11

This is an ongoing research program that began in March 2018. We have completed three projects with a fourth underway, all concerned with identifying and preventing suicidality among youths in juvenile detention. In the first project, we used quality improvement methods to develop a feasible, acceptable, and appropriate suicide prevention plan for a single, large juvenile detention center. 12 In this plan, behavioral health clinicians inside the juvenile detention center screen all youth for mental health needs, including suicidality. The clinicians then provide appropriate health services; one example is the Stanley and Brown Safety Planning Intervention, 13 which is a brief suicide prevention intervention that supports coping in times of crises.

The second project studied statewide efforts to prevent suicide in juvenile detention, with the primary goal to learn more about the quality of suicide prevention. 14 Within this study we also sought to identify barriers and facilitators to suicide prevention implementation. To get this information, we interviewed 80% of detention center leaders and staff in one state. We learned that suicide prevention practice adoption is high, but the quality of implementation in line with national guidelines is low.  A primary concern with respect to suicide prevention implementation barriers was the lack of behavioral health support available in juvenile detention centers. 15 Only half of juvenile detention centers had any amount of behavioral health support.

In project 3 we sought to understand suicide prevention barriers and facilitators with input from centers with varying levels of support from behavioral health clinicians. 16 We learned that leadership support of suicide prevention is key in supporting implementation efforts across centers. The findings also suggest that training frontline supervisors to better support frontline staff in the delivery of suicide prevention may improve intervention sustainability for centers without behavioral health clinicians. 16

We are now conducting project 4. Project 4 is a 5-year National Institute of Mental Health (NIMH) career development award that aims to use community-based participatory methods to develop a Zero Suicide prevention model for juvenile detention (K23MH129321). In this project, we will work with young Black youth connected to the juvenile-legal system to understand their preferences for suicide prevention, develop a model for suicide prevention that incorporates the lessons learned from all four projects, and then pilot the model in two detention centers. The final output of the current project is a manualized Zero Suicide model that is primed for implementation and sustainment in juvenile detention centers.

Significance

Through the development of our Zero Suicide model, we hope to increase the adoption and implementation of evidence-based suicide prevention in juvenile detention centers. As that occurs, we expect that 1) fewer suicidal youth will be detained because center staff will better understand the harms associated with detaining those youth and seek alternative placement and 2) fewer youth will experience suicidality when detained.

Our work is informed by community-based participatory research designs. As such, in each project, we have aimed to center the needs of Black youth who are, due to structural racism, overrepresented in juvenile detention. We have sought to include community voices throughout our research program. By developing a model that centers the needs of Black youth specifically, we are explicitly focusing on equity.

Demonstrated benefits are those that have been observed and are verifiable.

Potential benefits are those logically expected with moderate to high confidence.

We aim to develop culturally appropriate suicide prevention practices. potential.

The Zero Suicide model will be a service for individuals in juvenile detention. potential.

The Zero Suicide model aims to reduce youth suicidality. potential.

The Zero Suicide model aims to connect youth to mental health care delivery for those who need help. potential.

The Zero Suicide model aims to increase the quality of suicide prevention practices in juvenile detention. potential.

By reducing youth suicidality, we hope to improve life expectancy. potential.

The Zero Suicide model will take a public health approach to mental health, making considerations for universal, indicated, and intensive mental health services. potential.

By reducing suicidality among youth, we hope to increase the life expectancy of youth, which will benefit society financially by having these young people in our economy longer. potential.

We have published several papers that guide suicide prevention in juvenile detention. demonstrated.

We hope that our Zero Suicide model will become the new standard for suicide prevention in juvenile detention and will be adopted by county and state agencies that oversee juvenile detention facilities. potential.

This research has clinical, community, economic, and policy implications. The framework for these implications was derived from the Translational Science Benefits Model created by the Institute of Clinical & Translational Sciences at Washington University in St. Louis.

We hope to adapt existing suicide prevention programs to ensure they meet the needs of Black youth, thereby improving the effectiveness of those interventions and ultimately saving lives.

The ultimate goal of our work is to minimize the number of youth who have mental health concerns from entering juvenile detention, and to prevent mental health concerns from developing among those who do. Our Zero Suicide model will support juvenile detention centers in do just that.

All youth deserve an opportunity to see adulthood. We hope that our program will increase the life expectancies of youth in juvenile-legal systems, ultimately making this world a better place for all.

While there are standards for suicide prevention for juvenile detention, we want them to be enhanced. We hope this work will help with this.

References Arrow Down

• Heron M. Deaths: Leading Causes for 2016.
• Abram KM, Choe JY, Washburn JJ, Teplin LA, King DC, Dulcan MK. Suicidal Ideation and Behaviors Among Youth in Juvenile Detention. J Am Acad Child Adolesc Psychiatry . 2008;47(3):291-300.
• Livanou M, Furtado V, Winsper C, Silvester A, Singh SP. Prevalence of Mental Disorders and Symptoms Among Incarcerated Youth: A Meta-Analysis of 30 Studies. Int J Forensic Ment Health . 2019;18(4):400-414.
• Hayes LM. Juvenile Suicide in Confinement—Findings from the First National Survey. Suicide Life Threat Behav . 2009;39(4):353-363.
• Wasserman GA, McReynolds LS. Suicide Risk at Juvenile Justice Intake. Suicide Life Threat Behav . 2006;36(2):239-249.
• Esposito CL, Clum GA. Social support and problem-solving as moderators of the relationship between childhood abuse and suicidality: Applications to a delinquent population. J Trauma Stress . 2002;15(2):137-146.
• Rovner J. Racial Disparities in Youth Commitments and Arrests. The Sentencing Project. Published April 1, 2016. Accessed May 29, 2024. https://www.sentencingproject.org/reports/racial-disparities-in-youth-commitments-and-arrests/
• State detention rates by race/ethnicity, 2019 | Office of Juvenile Justice and Delinquency Prevention. Accessed May 29, 2024.
• Office of the Surgeon General (US), National Action Alliance for Suicide Prevention (US). 2012 National Strategy for Suicide Prevention: Goals and Objectives for Action: A Report of the U.S. Surgeon General and of the National Action Alliance for Suicide Prevention . US Department of Health & Human Services (US); 2012. Accessed May 29, 2024. http://www.ncbi.nlm.nih.gov/books/NBK109917/
• Richards JE, Simon GE, Boggs JM, et al. An implementation evaluation of “Zero Suicide” using normalization process theory to support high-quality care for patients at risk of suicide. Implement Res Pract . 2021;2:26334895211011769. doi:10.1177/26334895211011769
• Alegria M, Vallas M, Pumariega A. Racial and Ethnic Disparities in Pediatric Mental Health. Child Adolesc Psychiatr Clin N Am . 2010;19(4):759-774.
• Rudd BN, George JM, Snyder S, et al. Harnessing Quality Improvement and Implementation Science to Support the Implementation of Suicide Prevention Practices in Juvenile Detention. Psychotherapy . 2022;59(2):150-156.
• Home. Stanley-Brown Safety Planning Intervention. Accessed May 29, 2024. https://suicidesafetyplan.com/
• Rudd BN, Witzig J, Goff CN, et al. A Statewide Evaluation of the Implementation of Evidence-Based Suicide Prevention Guidelines in Juvenile Detention Centers. Psychiatr Serv Wash DC . Published online February 19, 2024:appips20220490.
• Rudd BN. Protecting the most vulnerable: Suicide prevention in the justice system. Symposium presented at: 55th annual meeting of the Association for Behavioral and Cognitive Therapies; November 2021; Virtual.
• Rudd BN, Stern DH, Potter EN, Goff CN. The zero suicide model for juvenile detention: Leadership, training, and staff attitudes matter. Presented at: November 18, 2021.
• Luke DA, Sarli CC, Suiter AM, et al. The Translational Science Benefits Model: A New Framework for Assessing the Health and Societal Benefits of Clinical and Translational Sciences. Clin Transl Sci . 2018;11(1):77-84.

College & Research Libraries ( C&RL ) is the official, bi-monthly, online-only scholarly research journal of the Association of College & Research Libraries, a division of the American Library Association.

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Leo S. Lo is Dean, College of University Libraries and Learning Sciences at the University of New Mexico, email: [email protected] .

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Evaluating AI Literacy in Academic Libraries: A Survey Study with a Focus on U.S. Employees

Leo S. Lo *

This survey investigates artificial intelligence (AI) literacy among academic library employees, predominantly in the United States, with a total of 760 respondents. The findings reveal a modest self-rated understanding of AI concepts, limited hands-on experience with AI tools, and notable gaps in discussing ethical implications and collaborating on AI projects. Despite recognizing the benefits, readiness for implementation appears low among participants. Respondents emphasize the need for comprehensive training and the establishment of ethical guidelines. The study proposes a framework defining core components of AI literacy tailored for libraries. The results offer insights to guide professional development and policy formulation as libraries increasingly integrate AI into their services and operations.

Introduction

In a world increasingly dictated by algorithms, artificial intelligence (AI) is not merely a technological phenomenon, it is a transformative force that redefines our intellectual, social, and professional landscapes (McKinsey and Company, 2023). The rapid integration of AI in our everyday lives has profound implications for higher education, a sector entrusted with preparing individuals to navigate, contribute to, and thrive in this AI-driven era. From personalized learning environments to automated administrative tasks, AI’s influence in higher education is omnipresent and its potential boundless. However, this potential can only be harnessed effectively if those at the frontline of academia—our educators, researchers, administrators, and, notably, academic library employees—are equipped with the necessary AI literacy (UNESCO, 2021). Without an understanding of AI’s principles, capabilities, and ethical considerations, higher education risks falling prey to AI’s pitfalls rather than leveraging its benefits.

The potential risks and benefits underscore a pressing need to scrutinize and elevate AI literacy within the higher education community—a task that begins with understanding its current state. As facilitators of information and knowledge, academic library employees stand at the crossroads of this AI revolution, making their AI literacy an imperative, not a choice, for the future of higher education.

AI Literacy: Context and Background

In an era marked by exponential growth in digital technology, the concept of literacy has evolved beyond traditional reading and writing skills to encompass a wide array of digital competencies. One such competency, which is gaining critical importance in higher education, is AI literacy. With AI systems beginning to permeate every facet of university operations—from learning management systems to research analytics—the ability to understand and navigate these AI tools has become an essential skill for academic library employees.

AI literacy, a subset of digital literacy, specifically pertains to understanding AI’s principles, applications, and ethical considerations. It involves not only the ability to use AI tools effectively, but also the capacity to evaluate their outputs critically, to understand their underlying mechanisms, and to contemplate their ethical and societal implications. AI literacy is not just for computer professionals; as Lo (2023b) and Cetindamar et al. (2022) emphasize, operationalizing AI literacy for non-specialists is essential.

The significance of AI literacy in higher education is underscored by several contemporary trends and challenges. Companies and governments globally are engaged in fierce competition to stay at the forefront of AI integration. Concurrently, the rapid proliferation of AI is giving rise to a host of ethical and privacy concerns that require informed stewardship (Cox, 2022). Furthermore, the COVID-19 pandemic has accelerated the digital transformation of higher education, leading to an increased reliance on AI technologies for remote learning and operations. This reliance further points to the necessity of AI literacy among academic library employees, who play a pivotal role in facilitating online learning and research.

As artificial intelligence proliferates across higher education, developing AI literacy is increasingly recognized as a priority to prepare students, faculty, staff, and administrators to harness AI’s potential, while mitigating risks (Ng et al., 2021). Hervieux and Wheatley’s (2021) 2019 study (n=163) found that academic librarians require more training regarding artificial intelligence and its potential applications in libraries. The U.S. Department of Education’s recent report (2023) on AI emphasizes the growing importance of AI literacy for educators and students, highlighting the necessity of understanding and integrating AI technologies in educational settings. This report aligns with the broader discourse on AI literacy and emphasizes the need to equip library professionals with skills needed to evaluate and utilize AI tools effectively (Lo, 2023a).

While efforts to promote AI literacy are growing, the required content for different target groups remains ambigu­ous. Some promising measurement tools have been proposed, such as Pinski and Benlian’s (2023) multidimensional scale assessing perceived knowledge of AI technology, processes, collaboration, and design. However, further validation of AI literacy assessments is required. Developing rigorous definitions and measurements is crucial for implementing effective AI literacy initiatives.

Ridley and Pawlick-Potts (2021) put forth the concept of algorithmic literacy, involving understanding algorithms and their influence, recognizing their uses, assessing their impacts, and positioning individuals as active agents rather than passive recipients of algorithmic decision-making. They propose libraries can contribute to algorithmic literacy by integrating it into information literacy education and supporting explainable AI.

Ocaña-Fernández et al. (2019) argued curriculum and skills training changes are critical to prepare students and faculty for an AI future, though also warn about digital inequality issues. Laupichler et al.’s (2022) scoping review reveals efforts to teach foundational AI literacy to non-specialists are still in formative stages. Proposed essential skills vary considerably across frameworks, and robust evaluations of AI literacy programs are lacking. Findings indicate that carefully designed AI literacy courses show promise for knowledge gains; however, research substantiating appropriate frameworks, core competencies and effective instructional approaches for diverse audiences remains an open need.

Within libraries, Heck et al. (2019) discussed the interplay of information literacy and AI. They propose that AI could aid information literacy teaching through timely feedback and tracking skill development, but note that common evaluation approaches would need establishing first. Information literacy empowers learners to actively engage with, not just passively consume from, AI systems. Lo (2023c) proposed a framework to utilize prompt engineering to enhance information literacy and critical thinking skills.

Oliphant (2015) examined intelligent agents for library reference services. The analysis found they rapidly retrieve information but lack human evaluation abilities. Findings suggest librarians will need to guide users in critically evaluating AI-generated results, indicating that information literacy instruction remains crucial. Furthermore, Lund et al. (2023) discuss the ethical implications of using large language models, such as ChatGPT, in scholarly publishing, emphasizing the need for ethical considerations and the potential impact of AI on research practices.

While research is still emerging, initial findings highlight the need for rigorous, tailored AI literacy initiatives encompassing technical skills, critical perspectives, and ethical considerations. As AI becomes further entwined with education and work, developing validated frameworks, assessments, and instructional approaches to enhance multidimensional AI literacy across contexts and roles is an urgent priority. This study seeks to contribute by investigating AI literacy specifically among academic library employees.

Purpose of the Study

The rapid pace of AI development and integration in higher education heightens the need to address this research gap. As AI continues to evolve and permeate further into academic libraries, the demand for AI-literate library employees will only increase. Failure to understand the current state of AI literacy, and to identify the gaps, could result in a significant skills deficit that would impedes the effective utilization of AI in academic libraries.

In light of this, the purpose of this study is to embark on an investigation of AI literacy among academic library employees. The study seeks to answer the following critical research questions:

• What is the current level of AI literacy among academic library employees?
• What gaps exist in their AI literacy, and how can these gaps be addressed through professional development and training programs?
• What are their perceptions of generative AI, and what implications do they foresee for the library profession?

By addressing these questions, this study aims to fill a research gap and provide insights that can inform policy and practice in higher education. It strives to shed light on the competencies that academic library employees possess, identify the gaps that need to be addressed, and propose strategies for enhancing AI literacy among this essential group of higher education professionals.

Theoretic Framework

The Technological Pedagogical Content Knowledge (TPACK) framework developed by Mishra and Koehler (2006) serves as the theoretical foundation for this study. TPACK has also been advocated as a useful decision-making structure for librarians evaluating instructional technologies (Sobel & Grotti, 2013).

Mishra and Koehler (2006) explain that TPACK involves flexible, context-specific application of technology, pedagogy, and content knowledge. It goes beyond isolated knowledge of the concepts to an integrated understanding. TPACK development requires moving past viewing technology as an “add-on” and focusing on the connections between technology, content, and pedagogy in particular educational contexts.

In the context of this study, the researcher applied the TPACK framework to examine AI literacy specifically among academic library professionals. The three key components of the TPACK framework are interpreted as:

• Technological Knowledge (TK)—Knowledge about AI itself, including its principles, capabilities, and limitations. This encompasses understanding AI as a technology and its potential applications in library settings.
• Pedagogical Knowledge (PK)—Knowledge about how AI can be used to enhance library services and facilitate learning. This relates to understanding how AI can be integrated into library services to improve user experience, streamline operations, and support learning.
• Content Knowledge (CK)—Knowledge about the library’s content and services. This involves perceiving the potential impact of AI on the library’s content and services, and how AI can enhance their management and delivery.

This tailored application of the TPACK framework will allow a multidimensional assessment of AI literacy among academic library employees. It facilitates examining employees’ understanding of AI as a technology (TK), perceptions of how AI can enhance library services (PK), and the potential impact of AI on the library’s content and services (CK).

Significance of the Study

The significance of this study lies in its potential to contribute to academic library policy, practice, and theory in several ways. Firstly, it utilizes the TPACK framework to evaluate AI literacy among academic library employees, identifying competencies, gaps, and necessary strategies. This insight is crucial for designing effective professional development programs, as well as for resource allocation. Secondly, it adds to the discourse on digital literacy in higher education by specifically focusing on AI literacy, aiding in understanding its role and implications. Thirdly, the study provides insights into the ethical, practical, and opportunity dimensions of AI technology integration in libraries, informing best practices and guidelines for its responsible use. Lastly, by applying the TPACK framework to AI literacy in libraries, the study expands its theoretical applications and offers a robust basis for future research in technology integration in academic settings.

Methodology

Research design.

This study employs a survey-based approach to explore AI literacy among academic library employees, chosen for its ability to quickly gather extensive data across a geographically diverse group. The method aligns with the TPACK framework, highlighting the integration of technological, pedagogical, and content knowledge. Surveys facilitate the collection of standardized data, allowing for comparisons across different roles and demographics. This design is particularly effective for descriptive research in higher education, making it suitable for assessing the current state of AI literacy in academic libraries.

Participants

The researcher utilized a comprehensive approach to recruit a diverse group of academic library employees for the survey. This involved posting on professional listservs across various roles and regions in librarianship (Appendix A), as well directly contacting directors of prominent library associations: the Association of Research Libraries (ARL), the Greater Western Library Alliance (GWLA), and the New Mexico Consortium of Academic Libraries (NMCAL). These organizations represent a broad spectrum of academic libraries in terms of size, location, and type. The directors were requested to share the survey with their staff, thus ensuring a wide-reaching and representative sample for the study.

Data Collection

Data collection was facilitated through a custom-designed survey instrument, which was built and administered using the Qualtrics platform (Appendix B). The survey itself was developed to address the study’s research questions and was structured into four main sections, each focusing on a specific aspect of AI literacy among academic library employees.

The first section sought to capture respondents’ understanding and knowledge of AI, including their familiarity with AI concepts and terminology. The second section focused on respondents’ practical skills and experiences with AI tools and applications in professional settings. The third section aimed to identify areas of AI literacy where respondents felt less confident, signaling potential gaps in knowledge or skills that could be addressed through professional development initiatives. Finally, the last section explored respondents’ perspectives on the ethical implications and challenges presented by AI technologies in the library context.

The survey employed a mix of question types to engage respondents and capture nuanced data. These included Likert-scale questions, multiple choice, and open-ended questions. Prior to the full-scale administration, the survey was pilot-tested with a small group of academic library employees to ensure clarity, relevance, and appropriateness of the questions.

The survey questions were designed to tap into different dimensions of the TPACK framework. For instance, questions asking about practical experiences with AI tools and self-identified areas of improvement indirectly assess the intersection of technological and pedagogical knowledge (TPK), as they relate to AI.

Upon finalizing the survey, an invitation to participate, along with a link to the survey, was distributed via the listservs and direct outreach methods. The survey remained open for two weeks, with reminders sent out at regular intervals to maximize the response rate.

Limitations

While the study offers insights into AI literacy among academic library employees, it is crucial to acknowledge its limitations. Firstly, given the survey’s self-report nature, the findings may be subject to social desirability bias, where respondents might have over- or under-estimated their knowledge or skills in AI.

Secondly, despite best efforts to reach a wide range of academic library employees, the sample may not be entirely representative of the population. The voluntary nature of participation, coupled with the distribution methods used, may have skewed the sample towards those with an existing interest or engagement in AI.

Moreover, while the use of professional listservs and direct outreach to library directors helped widen our reach, this strategy might have excluded those academic library employees who are less active, or not included, in these communication channels. The inclusion of Canadian libraries through the Association of Research Libraries suggests a small number of non-U.S. respondents.

Finally, the rapidly evolving nature of AI and its applications in libraries means that our findings provide a snapshot at a specific point in time. As AI continues to advance and integrate more deeply into academic libraries, the landscape of AI literacy among library employees is likely to shift, necessitating ongoing research in this area.

These limitations, while important to note, do not invalidate our findings. Instead, they offer points of consideration for interpreting the results and highlight areas for future research to build on our understanding of AI literacy among academic library employees.

Results and Analysis

Descriptive statistics.

The survey drew a diverse response: 760 participants started the survey, 605 completed it. The participants represented a cross-section of the academic library landscape, with the majority (45.20%) serving in Research Universities. A significant proportion also hailed from institutions offering both graduate and undergraduate programs (29.64%) and undergraduate-focused Colleges or Universities (10.76%). Community Colleges and specialized professional schools (e.g., Law, Medical) were represented as well, albeit to a lesser extent.

Over half of the respondents (61.25%) were from libraries affiliated with the Association of Research Libraries (ARL), signifying an extensive representation from research-intensive institutions. Respondents were predominantly from larger academic institutions. Those serving in institutions with enrollments of 30,000 or more made up the largest group (30.67%), closely followed by those in institutions with enrollments ranging from 10,000 to 29,999 (34.66%).

As for professional roles, the survey drew heavily from the library specialists or professionals (60.99%) who directly support the academic community’s research, learning, and teaching needs. Middle (20.00%) and senior (9.09%) management personnel were also well-represented, providing a leadership perspective to the survey insights.

Most of the respondents were primarily involved in Reference and Research Services (25.17%) or Library Instruction and Information Literacy (24.34%)—two areas integral to the academic support infrastructure.

In terms of professional experience, participants exhibited a broad range, from novices with less than a year’s experience (2.81%) to seasoned veterans with over 20 years in the field (22.68%).

The survey group was highly educated, with most holding a master’s degree in library and information science (65.51%), and a significant number having completed a doctoral degree or a master’s in another field.

The survey also collected demographic information. A substantial majority identified as female (71.97%), and the largest age group was 35–44 years (27.97%). While the majority identified as White (76.11%), other ethnicities, including Asian, Black or African American, and Hispanic or Latino, were also represented.

This diverse participant profile offers a broad-based view of AI literacy in the academic library landscape, setting the stage for insightful findings and discussions.

RQ 1 AI Literacy Levels

At a broad level, participants expressed a modest understanding of AI concepts and principles, with a significant portion rating their knowledge at an average level. However, the number of respondents professing a high understanding of AI was quite small, revealing a potential area for further training and education.

A similar pattern was observed when participants were queried about their understanding of generative AI specifically. This suggests that while librarians have begun to grasp AI and its potential, there is a considerable scope for growth in terms of knowledge and implementation (Figure 1).

Regarding the familiarity with AI tools, most participants had a moderate level of experience (30.94%). Only a handful of participants reported a high level of familiarity (3.87%), signaling an opportunity for more hands-on training with these tools.

In examining the prevalence of AI usage in the library sector, the researcher found a varied landscape. While some technologies have found significant adoption, others remain relatively unused. Notably, Chatbots and text or data mining tools were the most widely used AI technologies.

Participants’ understanding of specific AI concepts followed a similar trend. More straightforward concepts such as Machine Learning and Natural Language Processing had a higher average rating, whereas complex areas like Deep Learning and Generative Adversarial Networks were less understood. This trend underscores the need for targeted educational programs on AI in library settings.

Notably, there was almost a nine percent drop in responses from the previous questions to the questions that asked about the more technical aspects of AI. This could signify a gap in knowledge or comfort level with these topics among the participants.

In the professional sphere, AI tools have yet to become a staple in library work. The majority of participants do not frequently use these tools, with 41.79% never using generative AI tools and 28.01% using them less than once a month. This might be attributed to a lack of familiarity, resources, or perceived need. However, for those who do use them, text generation and research assistance are the primary use cases.

Concerns about ethical issues, quality, and accuracy of generated content, as well as data privacy, were prevalent among the participants. This finding indicates that while there’s interest in AI technologies, the perceived challenges are significant barriers to full implementation and adoption.

In their personal lives, AI tools have yet to make a significant impact among the participants. The majority (63.98%) reported using these tools either ‘less than once a month’ or ‘never.’ This could potentially reflect the current state of AI integration in non-professional or leisurely activities, and may change as AI continues to permeate our everyday lives.

A chi-square test of independence was performed to examine the relation between the position of the respondent and the understanding of AI concepts and principles. The relation between these variables was significant, χ 2 (16, N = 760) = 26.31, p = .05. This means that the understanding of AI concepts and principles varies depending on the position of the respondent.

The distributions suggest that—while there is a significant association between the position of the respondent and their understanding of AI concepts and principles—the majority of respondents across all positions have a moderate understanding of AI. However, there are differences in the proportions of respondents who rate their understanding as high or very high, with Senior Management and Middle Management having higher proportions than the other groups.

There is also a significant relation between the area of academic librarianship and the understanding of AI concepts and principles, χ²(36, N = 760) = 68.64, p = .00084. This means that the understanding of AI concepts and principles varies depending on the area of academic librarianship. The distributions show that there are differences in the proportions of respondents who rate their understanding as high or very high, with Administration or management and Library Instruction and Information Literacy having higher proportions than the other groups.

Furthermore, a Chi-Square test shows that the relation between the payment for a premium version of at least one of the AI tools and the understanding of AI concepts and principles is significant, χ²(4, N = 539) = 85.42, p < .001. The distributions suggest that respondents who have paid for a premium version of at least one of the AI tools have a higher understanding of AI concepts and principles compared to those who have not. This could be because those who have paid for a premium version of an AI tool are more likely to use AI in their work or personal life, which could enhance their understanding of AI. Alternatively, those with a higher understanding of AI might be more likely to see the value in paying for a premium version of an AI tool.

It’s important to note that these findings are based on the respondents’ self-rated understanding of AI, which may not accurately reflect their actual understanding. Further research could involve assessing the respondents’ understanding of AI through objective measures. Additionally, other factors not considered in this analysis, such as the respondent’s educational background, years of experience, and exposure to AI in their work, could also influence their understanding of AI.

RQ2 Identifying Gaps

In this section, the researcher delved deeper into the gaps in knowledge and confidence among academic library professionals regarding AI applications. These gaps highlight the urgent need for targeted professional development and training in AI literacy.

Confidence Levels in Various Aspects of AI

The survey data pointed to moderate levels of confidence across a spectrum of AI-related tasks, indicating room for growth and learning. For evaluating ethical implications of using AI, a modest 30.12% of respondents felt somewhat confident (levels 4 and 5 combined), while 29.50% were not confident (levels 1 and 2 combined), and the largest group (39.38%) remained neutral.

Discussing AI integration revealed similar patterns. Here, 31.1% reported high confidence, 34.85% expressed low confidence, and the remaining 33.06% were neutral. These distributions suggest an overall hesitation or lack of assurance in discussing and ethically implementing AI, potentially indicative of inadequate training or exposure to these topics.

When it came to collaborating on AI-related projects, fewer respondents (31.39%) felt confident, while 40.16% reported low confidence, and 28.46% chose a neutral stance. This might point to the necessity of not only individual proficiency in AI but also the need for collaborative skills and shared understanding among teams working with AI.

Troubleshooting AI tools and applications emerged as the most significant gap, with 69.76% rating their confidence as low and only 10.9% expressing high confidence. This highlights an essential area for targeted training, as troubleshooting is a fundamental aspect of successful technology implementation.

Reflecting on Professional Development and Training in AI

Approximately one-third of survey participants have engaged in AI-focused professional development, showcasing several key themes:

• Modes of Training: Librarians access training via various formats, including webinars, workshops, and self-guided learning. Online options are popular, providing accessibility for diverse professionals.
• AI Tools and Applications: Training sessions mainly introduce tools like ChatGPT and others, with an emphasis on functionality and applications in academia.
• Ethical Implications: Sessions often address ethical concerns such as bias and privacy, and the potential misuse of ‘black box’ AI models.
• Integration into Librarian Workflows: Programs explore AI’s integration into library work, including instruction, cataloging, and citation analysis.
• AI Literacy: There is a recurring focus on understanding and teaching AI concepts, tied to broader information literacy discussions.
• AI in Instruction: Training includes using AI tools in library instruction and understanding its impacts on academic integrity.
• Community of Practice: Responses highlight collaborative learning, suggesting a communal approach to understanding AI’s challenges and opportunities.
• Self-guided Learning: Some librarians actively pursue independent learning opportunities, reflecting a proactive stance on AI professional development.

The findings emphasize the multifaceted nature of AI in libraries, underlining the need for ongoing, comprehensive professional development. This includes addressing both technical and ethical aspects, equipping librarians with practical AI skills, and fostering a supportive community of practice.

A Chi-square test examining the relationship between the respondents’ positions and their participation in any training focused on generative AI (χ²(4, N = 595) = 26.72, p < .001) indicates a significant association. Upon examining the data, the proportion of respondents who have participated in training or professional development programs focused on generative AI is highest among those in Senior Management (47.27%), followed by Specialist or Professional (37.40%), Middle Management (29.75%), and Other (16.67%). The proportion is lowest among Support Staff or Administrative (3.70%).

This suggests that individuals in higher positions, such as Senior Management and Specialist or Professional roles, are more likely to have participated in training or professional development programs focused on generative AI. This could be due to a variety of reasons, such as these roles potentially requiring a more in-depth understanding of AI and its applications, or these individuals having more access to resources and opportunities for such training. On the other hand, Support Staff or Administrative personnel are less likely to have participated in such programs, which could be due to less perceived need or fewer opportunities for training in these roles.

These findings highlight the importance of providing access to training and professional development opportunities focused on AI across all roles in an organization, not just those in higher positions or those directly involved in AI-related tasks. This could help ensure a more widespread understanding and utilization of AI across the organization.

Despite these efforts, many participants did not feel adequately prepared to utilize generative AI tools professionally. A notable 62.91% disagreed to some extent with the statement: “I feel adequately prepared to use generative AI tools in my professional work as a librarian,” underscoring the need for more effective training programs.

Interestingly, the areas identified for further training weren’t just about understanding the basics of AI. Participants showed a clear demand for advanced understanding of AI concepts and techniques (13.53%), familiarity with AI tools and applications in libraries (14.21%), and addressing privacy and data security concerns related to generative AI (14.36%). This suggests that librarians are looking to move beyond a basic understanding and are keen to engage more deeply with AI.

Preferred formats for professional development opportunities leaned towards remote and flexible learning opportunities, such as online courses or webinars (26.02%) and self-paced learning modules (22.44%). This preference reflects the current trend towards digital and remote learning, providing a clear direction for future training programs.

Notably, almost half of the participants (43.99%) rated the need for academic librarians to receive training on AI tools and applications within the next twelve months as ‘extremely important.’ This emphasis on urgency indicates a significant and immediate gap to be addressed.

In summary, a deeper analysis of the data reveals a landscape where academic librarians possess moderate to low confidence in understanding, discussing, and handling AI-related tasks, despite some exposure to professional development in AI. This finding indicates the need for more comprehensive, in-depth, and accessible AI training programs. By addressing these knowledge gaps, the library community can effectively embrace AI’s potential and navigate its challenges.

RQ 3 Perceptions

The comprehensive results of our survey, as illustrated in Table 7, offer a detailed portrait of librarians’ perceptions towards the integration of generative AI tools in library services and operations.

When considering the potential benefits of AI, the responses indicate a degree of ambivalence, with 35.88% choosing a neutral stance. However, when we combine the categories of those who ‘agree’ and ‘strongly agree,’ we see that a significant portion, 49.84%, view AI as beneficial to a certain extent. Similarly, on the question of the importance of investment in AI, there is a notable inclination towards agreement, with 46.87% agreeing that investment is important to some degree.

However, this optimism is juxtaposed with concerns about readiness. When asked how prepared they feel to adopt generative AI tools within the forthcoming year, 70.03% of respondents (those who ‘strongly disagree’ or ‘disagree’) admit a lack of preparedness. This suggests that despite recognizing the potential value of AI, there are considerable obstacles to be overcome before implementation becomes feasible.

The uncertainty surrounding AI’s impact on libraries in the short-term further illuminates this complexity. A significant proportion of librarians (36.09%) chose a neutral response when asked to predict the impact of AI on academic libraries within the next twelve months. Nonetheless, there is a considerable group (41.06% who ‘agree’ or ‘strongly agree’) who foresee significant short-term impact.

A key finding from the survey was the collective recognition of the urgency to address ethical and privacy issues tied to AI usage. In fact, 74.34% of respondents, spanning ‘agree’ and ‘strongly agree,’ underscored the urgent need to address potential ethical and privacy concerns related to AI, highlighting the weight of responsibility librarians feel in maintaining the integrity of their services in the age of AI (Figure 2).

The qualitative responses provide a rich understanding of the perceptions of generative AI among library professionals and the implications they foresee for the library profession. The responses were categorized into several key themes, each of which is discussed below with relevant quotes from the respondents.

Ethical and Privacy Concerns

A significant theme that emerged from the responses was the ethical and privacy concerns associated with the use of generative AI tools in libraries. Respondents expressed apprehension about potential misuse of data and violations of privacy. As one respondent noted, “Library leaders should not rush to implement AI tools without listening to their in-house experts and operational managers.” Another respondent cautioned, “We need to be cautious about adopting technologies or practices within our own workflows that pose significant ethical questions, privacy concerns.”

Need for Education and Training

The need for education and training on AI for librarians was another prevalent theme. Respondents emphasized the importance of understanding AI tools and their implications before implementing them. One respondent suggested: “quickly education on AI is needed for librarians. As with anything else, there will be early adopters and then a range of adoption over time.” Another respondent highlighted the need for an AI specialist, stating, “I also think it would be valuable to have an AI librarian, someone who can be a resource for the rest of the staff.”

Potential for Misuse

Respondents expressed concern about the potential for misuse of AI tools, such as generating false citations or over-reliance on AI systems. They emphasized the importance of critical thinking skills, and cautioned against replacing human judgment and learning processes with AI. As one respondent put it, “Critical thinking skills and learning processes are vital and should not be replaced by AI.” Another respondent warned: “there are potential risks from misuse such as false citations being provided or too much dependence on systems.”

Concerns about Implementation

Several respondents expressed doubts about the ability of libraries to quickly and effectively implement AI tools. They cited issues such as frequent updates and refinements to AI tools, the need for significant investment, and the potential for AI to be used in ways that do not benefit the library or its users. One respondent noted, “the concern I have with AI tools is the frequent updates and refinements that occur. For libraries with small staff size, it seems daunting to keep up.”

Role of AI in Libraries

Some respondents suggested specific ways in which AI could be used in libraries, such as for collection development, instruction, and answering frequently asked questions. However, they also cautioned against viewing AI as a panacea for all library challenges. One respondent stated: “using them for FAQs will be more useful than answering a complicated reference question.”

Concerns about AI’s Impact on the Profession

Some respondents expressed concern that the use of AI could lead to job displacement or a devaluation of the human elements of librarianship. They suggested that AI should be used to complement, not replace, human librarians. One respondent expressed that, “I could see a future where only top research institutions have human reference librarians as a concierge service.”

Need for Critical Evaluation

Respondents emphasized the need for critical evaluation of AI tools, including understanding their limitations and potential biases. They suggested that libraries should not rush to implement AI without fully understanding its implications. One respondent advised: “the framing of AI usage as a forgone conclusion is concerning. It’s a tool, not a solution, and should not be implemented without due consideration.”

AI Literacy

Some respondents suggested that libraries have a role to play in teaching AI literacy to students and other library users. They emphasized the importance of understanding how AI tools work and how to use them responsibly. One respondent stated: “I think we need to teach AI literacy to students.” Another respondent echoed this sentiment, saying, “it is essential that we prepare our students to use generative AI tools responsibly.”

The perceptions of generative AI among library professionals are multifaceted, encompassing both the potential benefits and challenges of these technologies. While there is recognition of the potential of AI to enhance library services, there is also a strong emphasis on the need for ethical considerations, education and training, critical evaluation, and responsible use of these tools. The implications for the library profession are significant, with concerns about job displacement, the need for new skills and roles, and the potential for changes in library practices and services. These findings highlight the need for ongoing dialogue and research on the use of generative AI in libraries.

While library employees acknowledge the potential advantages of AI in library services, they also express concerns regarding readiness, and emphasize the urgency to address ethical and privacy considerations. These findings indicate the need for support systems, training, and resources to address readiness gaps, alongside rigorous discussion, and guidelines to navigate ethical and privacy issues as libraries explore the possibilities of AI integration.

Discussions

The survey results cast light on the current state of artificial intelligence literacy, training needs, and perceptions within the academic library community. The findings reveal a landscape of recognition for the potential of AI technologies, yet, simultaneously, a lack of in-depth understanding and preparedness for their adoption.

A detailed examination of the data reveals that a considerable number of library professionals self-assess their understanding of AI as sitting around, or below, the middle. While this does suggest a basic level of familiarity with AI concepts and principles, it likely falls short of the proficiency required to navigate the rapidly evolving AI landscape confidently and competently. This gap in understanding holds implications for the library field as AI continues to infiltrate various sectors and increasingly permeates library services and operations.

Moreover, an analysis of the familiarity of library professionals with AI tools lends further credence to this call for more comprehensive AI education initiatives. An understanding of AI extends beyond mere theoretical comprehension—it necessitates hands-on familiarity with AI tools and the ability to use and apply them in practice. Direct interaction with AI technologies provides an avenue for library professionals to bolster their practical understanding and thus equip them to incorporate these tools into their work more effectively.

However, formulating training initiatives that address these gaps is a multifaceted task. The AI usage in libraries is as diverse as the scope of AI applications themselves. From customer service chatbots, and text or data mining tools, to advanced technologies like neural networks and deep learning systems—each offers unique applications and therefore requires distinct expertise and understanding. Accordingly, training programs must be flexible and comprehensive, encompassing the full range of potential AI applications while also delving deep enough to provide a solid grasp of each specific tool’s functionality and potential uses.

The study also sheds light on the varying degrees of understanding across different AI concepts. Participants generally exhibited a higher level of comprehension for simpler AI concepts. However, their understanding waned when it came to more complex concepts, often the bedrock of cutting-edge AI applications. This variation in comprehension underscores the need for a stratified approach to AI education. Such an approach could start with foundational concepts and gradually progress towards more advanced topics, providing a scaffold on which a deeper understanding of AI can be built.

Addressing the AI literacy gap in the library sector thus requires a concerted approach—one that offers comprehensive and layered educational strategies that bolster both theoretical understanding and practical familiarity with AI. The aim should not only be to impart knowledge, but to empower library professionals to confidently navigate the AI landscape, to adopt and adapt AI technologies in their work effectively and—crucially —responsibly. Through such training and professional development initiatives, libraries can harness the potential of AI, ensuring they continue to be at the forefront of technological advancements.

As the focus shifts to the professional use of AI tools in libraries, the data reveal that their adoption is not yet commonplace. The use of AI tools—such as text generation and research assistance—are most reported, reflecting the immediate utility these technologies offer to librarians. However, a significant proportion of participants do not frequently use AI tools, indicating barriers to adoption. These barriers could include a lack of understanding or familiarity with these tools, a perceived lack of necessity for their use, or limitations in resources necessary for implementation and maintenance. To overcome these barriers, the field may need more than just providing education and resources. Demonstrating the tangible benefits and efficiencies AI tools can bring to library work could play a pivotal role in their wider adoption.

The data show a strong enthusiasm among librarians for professional development related to AI. While introductory training modalities are popular, the findings reveal a demand for more advanced, hands-on training. This need aligns with the complexity and rapid evolution of AI technologies, which require a deeper understanding to be fully leveraged in library contexts.

Furthermore, the findings highlight the importance of ethical considerations and the potential benefits of fostering communities of practice in AI training. With the increasing integration of AI technology into library services, the issues related to AI ethics will likely become more complex. Proactively addressing these concerns through in-depth, focused training can help libraries continue to serve as ethical stewards of information. Communities of practice provide a platform for shared learning, mutual support, and the pooling of resources, equipping librarians to better navigate the intricacies of AI integration.

Importantly, the data show that the diversity in librarians’ roles and contexts necessitates a tailored approach to AI training. Libraries differ in their services, target audiences, resources, and strategic goals, and so do their AI training needs. A one-size-fits-all approach to AI training may fall short. Future AI training could therefore take these variations into account, offering specialized tracks or modules catering to specific roles or institutional contexts.

Likewise, the perceptions surrounding the use of generative AI tools in libraries are intricate and multifaceted. While the potential benefits of AI are acknowledged and the importance of investing in its implementation recognized, there is also a pronounced lack of readiness to adopt these tools. This readiness gap could stem from various factors, such as a lack of technical skills, insufficient funding, or institutional resistance. Future research should delve into these possibilities to better understand and address this gap.

Library professionals express uncertainty about the short-term implications of AI for libraries. This could reflect the novelty of these technologies and a lack of clear use cases, or it could echo the experiences of early adopters. The findings also emphasize a heightened sense of urgency in addressing the ethical and privacy concerns associated with AI technologies. These concerns underline the necessity for ongoing dialogue, education, and policy development around AI use in libraries.

Conclusions and Future Directions

The results reveal an intricate landscape of AI understanding, usage, and perception in the library field. While the benefits of AI tools are acknowledged, a comprehensive understanding and readiness to implement these technologies remain less than ideal. This reality underlines the pressing need for an investment in targeted educational strategies and ongoing professional development initiatives.

Crucially, the wide variance in AI literacy, understanding of AI concepts, and hands-on familiarity with AI tools among library professionals points towards the need for a stratified and tailored approach to AI education. Future training programs must aim beyond just knowledge acquisition—they must equip library professionals with the capabilities to apply AI technologies in their roles effectively, ethically, and responsibly. Ethical and privacy concerns emerged as significant considerations in the adoption of AI technologies in libraries. Our findings reinforce the crucial role that libraries have historically played, and must continue to play, in advocating for ethical information practices.

The readiness gap in AI adoption uncovered by the study suggests a disconnect between understanding the potential of AI and the ability to harness it effectively. This invites a deeper investigation into potential barriers, including technical proficiency, resource allocation, and institutional culture, among others.

Framework and Key Competencies

This study presents a framework for defining AI literacy in academic libraries, encapsulating seven key competencies:

• Understanding AI System Capabilities and Limitations: Recognizing what AI can and cannot do, knowing its strengths and weaknesses.
• Identifying and Evaluating AI Use Cases: Discovering and assessing potential AI applications in library settings.
• Utilizing AI Tools Effectively and Appropriately: Applying AI technologies in library operations.
• Critically Assessing AI Quality, Biases, and Ethics: Evaluating AI for accuracy, fairness, and ethical considerations.
• Engaging in Informed AI Discussions and Collaborations: Participating knowledgeably in conversations and cooperative efforts involving AI.
• Recognizing Data Privacy and Security Issues: Understanding and addressing concerns related to data protection and security in AI systems.
• Anticipating AI’s Impacts on Library Stakeholders: Preparing for how AI will affect library users and staff.

This multidimensional definition of AI literacy for libraries provides a foundation for developing comprehensive training programs and curricula. For instance, the need to understand AI system capabilities and limitations highlighted in the definition indicates that introductory AI education should provide a solid grounding in how common AI technologies like machine learning work, where they excel, and their constraints. This conceptual comprehension equips librarians to set realistic expectations when evaluating or implementing AI.

The definition also accentuates that gaining practical skills to use AI tools appropriately should be a core training component. Hands-on learning focused on identifying appropriate applications, utilizing AI technologies effectively, and critically evaluating outputs can empower librarians to harness AI purposefully.

Moreover, emphasizing critical perspectives and ethical considerations reflects that AI training for librarians should move beyond technical proficiency. Incorporating modules examining biases, privacy implications, misinformation risks, and societal impacts is key for fostering responsible AI integration.

Likewise, the collaborative dimension of the definition demonstrates that cultivating soft skills for productive AI discussions and teamwork should be part of the curriculum. AI literacy has an important social element that training programs need to nurture.

Overall, this definition provides a skills framework that can inform multipronged, context-sensitive AI training tailored to librarians’ diverse needs. It constitutes an actionable guide for developing AI curricula and professional development that advance both technical and social aspects of AI literacy.

Future Research

Based on the findings and limitations of the current study, the following are specific recommendations for future research:

• Longitudinal Studies: This study provides a snapshot of AI literacy among academic library employees at a specific point in time. Future research could conduct longitudinal studies to track changes in AI literacy over time, which would provide insights into the effectiveness of interventions and the evolution of AI literacy in the library profession.
• Comparative Studies: This study focused on academic library employees. Future research could conduct comparative studies to examine AI literacy among different types of library employees (e.g., public library employees, school library employees), or among library employees in different countries. Such studies could provide insights into the factors that influence AI literacy and the strategies that are effective in different contexts.
• Intervention Studies: This study identified the need for education and training on AI. Future research could design and evaluate interventions aimed at enhancing AI literacy among library employees. Such studies could provide evidence-based recommendations for the development of training programs and resources.
• Ethical Considerations: This study highlighted ethical concerns about the use of AI in libraries. Future research could delve deeper into these ethical issues, examining the perspectives of different stakeholders (e.g., library users, library administrators) and exploring strategies for addressing these concerns.
• Impact of AI on Library Services: This study explored library employees’ perceptions of the potential impact of AI on library services. Future research could examine the actual impact of AI on library services, assessing the effectiveness of AI in enhancing user experience, streamlining operations, and supporting learning.

By pursuing these avenues for future research, we can continue to deepen our understanding of AI literacy in the library profession, inform strategies for enhancing AI literacy, and promote the effective and ethical use of AI in libraries.

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Appendix A. Recruitment—Listservs

• American Indian Library Association (AILA)
• American Libraries Association (ALA) Members
• Asian Pacific American Librarians Association (APALA)
• □ Members
• □ University Libraries Section
• □ Distance and Online Learning Section
• □ Instruction Section
• Association of Research Libraries (ARL) Directors Listserv
• Black Caucus American Library Association (BCALA)
• Chinese American Librarians Association (CALA)
• Greater Western Library Alliance (GWLA) Directors’ listserv
• Minnesota Institute Graduates (MIECL)
• New Mexico Consortium of Academic Libraries (NMCAL) Directors’ Listserv

Appendix B. AI and Academic Librarianship

Survey flow.

Standard: Block 1 (1 Question)

Block: Knowledge and Familiarity (12 Questions)

Standard: Perceived Competence and Gaps in AI Literacy (5 Questions)

Standard: Training on Generative AI for Librarians (6 Questions)

Standard: Desired Use of Generative AI in Libraries (7 Questions)

Standard: Demographic (10 Questions)

Standard: End of Survey (1 Question)

Start of Block: Block 1

Q1.1 Introduction

Dr. Leo Lo from the University of New Mexico is conducting a research project. You are invited to participate in a research study aiming to assess AI literacy among academic library employees, identify gaps in AI literacy that require further professional development and training, and understand the differences in AI literacy levels across different roles and demographic factors. Before you begin the survey, please read this Informed Consent Form carefully. Your participation in this study is voluntary, and you may choose to withdraw at any time without any consequences.

Artificial Intelligence (AI) refers to the development of computer systems and software that can perform tasks that would typically require human intelligence. These tasks may include problem-solving, learning, understanding natural language, recognizing patterns, perception, and decision-making

You are being asked to participate based of the following inclusion and exclusion criteria:

Inclusion Criteria:

• Currently employed as an employee in a college or university library setting.
• Willing and able to provide informed consent for participation in the study.

The Exclusion Criteria are as Follows:

• Librarian employees working in non-academic library settings (e.g., public libraries, school libraries, special libraries).
• Individuals who are not currently library employees or who are employed in non-library roles within academic institutions.

The purpose of this study is to evaluate the current AI literacy levels of academic librarians and identify areas where further training and development may be needed. The findings will help inform the design of targeted professional development programs and contribute to the understanding of AI literacy in the library profession.

If you agree to participate in this study, you will be asked to complete an online survey that will take approximately 15–20 minutes to complete. The survey includes questions about your AI knowledge, familiarity with AI tools and applications, perceived competence in using AI, and your opinions on training needs.

Potential Risks and Discomforts

There are no known risks or discomforts associated with participating in this study. Some questions might cause minor discomfort due to self-reflection, but you are free to skip any questions you prefer not to answer. Benefits While there are no direct benefits to you for participating in this study, your responses will help contribute to a better understanding of AI literacy among academic librarians and inform the development of relevant professional training programs.

Confidentiality

Your responses will be anonymous, and no personally identifiable information will be collected. Data will be stored securely on password-protected devices or encrypted cloud storage services, with access limited to the research team. The results of this study will be reported in aggregate form, and no individual responses will be identifiable. Your information collected for this project will NOT be used or shared for future research, even if we remove the identifiable information like your name.

Voluntary Participation and Withdrawal

Your participation in this study is voluntary, and you may choose to withdraw at any time without any consequences. Please note that if you decide to withdraw from the study, the data that has already been collected from you will be kept and used. This is necessary to maintain the integrity of the study and ensure that the data collected is reliable and valid.

Contact Information

If you have any questions or concerns about this study, please contact the principal investigator, Leo Lo, at [email protected] . If you have questions regarding your rights as a research participant, or about what you should do in case of any harm to you, or if you want to obtain information or offer input, please contact the UNM Office of the IRB (OIRB) at (505) 277-2644 or irb.unm.edu

By clicking “I agree” below, you acknowledge that you have read and understood the information provided above, had an opportunity to ask questions, and voluntarily agree to participate.

I agree (1)

I do not agree (2)

Skip To: End of Survey If Q1.1 = I do not agree

End of Block: Block 1

Start of Block: Knowledge and Familiarity

Q2.1 Artificial Intelligence

(AI) refers to the development of computer systems and software that can perform tasks that would typically require human intelligence. These tasks may include problem-solving, learning, understanding natural language, recognizing patterns, perception, and decision-making

Please rate your overall understanding of AI concepts and principles (using a Likert scale, e.g., 1 = very low, 5 = very high)

Q2.2 On a scale of 1 to 5, how would you rate your understanding of generative AI ? (1 = not at all knowledgeable, 5 = extremely knowledgeable)

Q2.3 Rate your familiarity with generative AI tools (e.g., ChatGPT, DALL-E, etc.) (using a Likert scale, e.g., 1 = not familiar, 5 = very familiar)

Q2.4 Which of the following AI technologies or applications have you encountered or used in your role as an academic librarian? (Select all that apply)

• □ Chatbots (1)
• □ Text or data mining tools (2)
• □ Recommender systems (3)
• □ Image or object recognition (4)
• □ Automated content summarization (5)
• □ Sentiment analysis (6)
• □ Speech recognition or synthesis (7)
• □ Other(please specify) (8) __________________________________________________

Q2.5 For each of the following AI concepts, indicate your understanding of the concept by selecting the appropriate response.

Q2.6 Which of the following generative AI tools have you used at least a few times? (Select all that apply)

• □ Text generation (e.g., ChatGPT) (1)
• □ Image generation (e.g., DALL-E, Mid Journey) (2)
• □ Music generation (e.g., OpenAI’s MuseNet) (3)
• □ Video generation (e.g. Synthesia) (4)
• □ Presentation generation (e.g. Tome) (5)
• □ Voice generation (e.g. Murf) (6)
• □ Data synthesis for research purposes (7)
• □ Other (please specify) (8) __________________________________________________

Display This Question:

If If Which of the following generative AI tools have you used at least a few times? (Select all that a… q://QID5/SelectedChoicesCount Is Greater Than 0

Q2.7 Have you ever paid for a premium version of at least one of the AI tools (for example, ChatGPT Plus; or Mid Journey subscription plan, etc.)

Q2.8 How frequently do you use generative AI tools in your professional work? (Select one)

Several times per week (2)

A few times per month (4)

Monthly (5)

Less than once a month (6)

Q2.9 For what purposes do you use generative AI tools in your professional work? (Select all that apply)

• □ Content creation (e.g., blog posts, social media updates) (1)
• □ Research assistance (e.g., literature reviews, data synthesis) (2)
• □ Data analysis or visualization (3)
• □ Cataloging or metadata generation (4)
• □ User support or assistance (e.g., chatbots, virtual reference) (5)
• □ Other (please specify) (6) __________________________________________________

Q2.10 On a scale of 1 to 5, how would you rate how reliable  generative AI tools have been in fulfilling your professional needs? (1 = not at all reliable, 5 = extremely reliable) 

Please explain your choice. 

1 (1) __________________________________________________

2 (2) __________________________________________________

3 (3) __________________________________________________

4 (4) __________________________________________________

5 (5) __________________________________________________

Q2.11 What level of concern do you have for the following potential challenges in implementing generative AI technologies in academic libraries? (Rate each challenge on a scale of 1 to 5, where 1 = not at all concerned and 5 = extremely concerned)

Q2.12 How frequently do you use generative AI tools in your personal life ? (Select one)

End of Block: Knowledge and Familiarity

Start of Block: Perceived Competence and Gaps in AI Literacy

Q3.1 On a scale of 1 to 5, how confident are you in your ability to evaluate the ethical implications of using AI in your library? (1 = not at all confident, 5 = extremely confident)

Q3.2 On a scale of 1 to 5, how confident are you in your ability to participate in discussions about AI integration within your library? (1 = not at all confident, 5 = extremely confident)

Q3.3 On a scale of 1 to 5, how confident are you in your ability to collaborate with colleagues on AI-related projects in your library? (1 = not at all confident, 5 = extremely confident)

Q3.4 On a scale of 1 to 5, how confident are you in your ability to troubleshoot issues related to AI tools and applications used in your library? (1 = not at all confident, 5 = extremely confident)

Q3.5 On a scale of 1 to 5, how confident are you in your ability to provide guidance to library users about AI resources and tools ? (1 = not at all confident, 5 = extremely confident)

End of Block: Perceived Competence and Gaps in AI Literacy

Start of Block: Training on Generative AI for Librarians

Q4.1 Have you ever participated in any training or professional development programs focused on generative AI?

If Q4.1 = Yes

Q4.2 Please briefly describe the nature and content of the training or professional development program(s) you attended.

________________________________________________________________

Q4.3 To what extent do you agree or disagree with the following statement: “ I feel adequately prepared to use generative AI tools in my professional work as a librarian .” (1 = strongly disagree, 5 = strongly agree)

Q4.4 In which of the following areas do you feel the need for additional training or professional development related to AI? (Select all that apply)

• □ Basic understanding of AI concepts and terminology (1)
• □ Advanced understanding of AI concepts and techniques (2)
• □ Familiarity with AI tools and applications in libraries (3)
• □ Ethical considerations of AI in libraries (4)
• □ Collaborating on AI-related projects (5)
• □ Addressing privacy and data security concerns related to generative AI (6)
• □ Troubleshooting AI tools and applications (7)
• □ Providing guidance to library users about AI resources (8)
• □ Other (please specify) (9) __________________________________________________

Q4.5 What types of professional development opportunities related to AI would be most beneficial to you? (Select all that apply)

• □ Online courses or webinars (1)
• □ In-person workshops or seminars (2)
• □ Conference presentations or panel discussions (3)
• □ Self-paced learning modules (4)
• □ Mentoring or coaching (5)
• □ Peer learning groups or communities of practice (6)
• □ Other (please specify) (7) __________________________________________________

Q4.6 How important do you think it is for academic librarians to receive training on generative AI tools and applications in the next 12 months ? (1 = not at all important, 5 = extremely important)

End of Block: Training on Generative AI for Librarians

Start of Block: Desired Use of Generative AI in Libraries

Q5.1 To what extent do you agree or disagree with the following statement: “ I believe generative AI tools have the potential to benefit library services and operations .” (1 = strongly disagree, 5 = strongly agree)

Q5.2 How important do you think it is for your library to invest in the exploration and implementation of generative AI tools ? (1 = not at all important, 5 = extremely important)

Q5.3 If you have any additional thoughts or suggestions on how your library could or should use (or not use) generative AI tools, please share them here.

Q5.4 How soon do you think your library should prioritize implementing generative AI tools and applications? (Select one)

Immediately (1)

Within the next 6 months (2)

Within the next year (3)

Within the next 2–3 years (4)

More than 3 years from now (5)

Not a priority at all (6)

Q5.5 In your opinion, how prepared is your library to adopt generative AI tools and applications in the next 12 months? (1 = not at all prepared, 5 = extremely prepared)

Q5.6 To what extent do you think generative AI tools and applications will have a significant impact on academic libraries within the next 12 months ? (1 = no impact, 5 = major impact)

Q5.7 How urgent do you feel it is for your library to address the potential ethical and privacy concerns related to the use of generative AI tools and applications? (1 = not at all urgent, 5 = extremely urgent)

End of Block: Desired Use of Generative AI in Libraries

Start of Block: Demographic

Q6.1 In which type of academic institution is your library located? (Select one)

Community college (1)

College or university (primarily undergraduate) (2)

College or university (graduate and undergraduate) (3)

Research university (4)

Specialized or professional school (e.g., law, medical) (5)

Other (please specify) (6) __________________________________________________

Q6.2 Is your library an ARL member library?

Q6.3 Approximately how many students are enrolled at your institution? (Select one)

Fewer than 1,000 (1)

1,000–4,999 (2)

5,000–9,999 (3)

10,000–19,999 (4)

20,000–29,999 (5)

30,000 or more (6)

Q6.4 What is your current role or position in your organization? (Select one)

Senior management (e.g. Director, Dean, associate dean/director) (1)

Middle management (e.g. department head, supervisor, coordinator) (2)

Specialist or professional (e.g., librarian, analyst, consultant) (3)

Support staff or administrative (4)

Other (please specify) (5) __________________________________________________

Q6.5 In which area of academic librarianship do you primarily work? (Select one)

Administration or management (1)

Reference and research services (2)

Technical services (e.g., acquisitions, cataloging, metadata) (3)

Collection development and management (4)

Library instruction and information literacy (5)

Electronic resources and digital services (6)

Systems and IT services (7)

Archives and special collections (8)

Outreach, marketing, and communications (9)

Other (please specify) (10) __________________________________________________

Q6.6 How many years of experience do you have as a library employee?

Less than 1 year (1)

1–5 years (2)

6–10 years (3)

11–15 years (4)

16–20 years (5)

More than 20 years (6)

Q6.7 What is the highest level of education you have completed? (Select one)

High school diploma or equivalent (1)

Some college or associate degree (2)

Bachelor’s degree (3)

Master’s degree in library and information science (e.g., MLIS, MSLS) (4)

Master’s degree in another field (5)

Doctoral degree (e.g., PhD, EdD) (6)

Other (please specify) (7) __________________________________________________

Q6.8 What is your gender? (Select one)

Non-binary / third gender (3)

Prefer not to say (4)

Q6.9 What is your age range?

Under 25 (1)

65 and above (5)

Q6.10 How do you describe your ethnicity? (Select one or more)

• □ American Indian or Alaskan Native (1)
• □ Asian (2)
• □ Black or African American (3)
• □ Hawaiian or Other Pacific Islander (4)
• □ Hispanic or Latino (5)
• □ White (6)
• □ Prefer not to say (7)
• □ Other (8) __________________________________________________

End of Block: Demographic

Start of Block: End of Survey

Q7.1 Thank you for participating in our survey!

Your input is incredibly valuable to us and will contribute to our understanding of AI literacy among academic librarians. We appreciate the time and effort you have taken to share your experiences and opinions. The information gathered will help inform future professional development opportunities and address potential gaps in AI knowledge and skills.

We will carefully analyze the responses and share the findings with the academic library community. If you have any further comments or questions about the survey, please do not hesitate to contact us at [email protected].

Once again, thank you for your contribution to this important research. Your insights will help shape the future of AI in academic libraries.

Best regards,

University of New Mexico

End of Block: End of Survey

* Leo S. Lo is Dean, College of University Libraries and Learning Sciences at the University of New Mexico, email: [email protected] . ©2024 Leo S. Lo, Attribution-NonCommercial (https://creativecommons.org/licenses/by-nc/4.0/) CC BY-NC.

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